E 151
OULU 2014
UNIV ER S IT Y OF OULU P. O. BR[ 00 FI-90014 UNIVERSITY OF OULU FINLAND
U N I V E R S I TAT I S
S E R I E S
SCIENTIAE RERUM NATURALIUM
Professor Esa Hohtola
HUMANIORA
University Lecturer Santeri Palviainen
TECHNICA
Postdoctoral research fellow Sanna Taskila
ACTA
AFFECTIVE AND EFFECTIVE
COLLABORATIVE LEARNING
PROCESS-ORIENTED DESIGN STUDIES IN
A TEACHER EDUCATION CONTEXT
MEDICA
Professor Olli Vuolteenaho
SCIENTIAE RERUM SOCIALIUM
University Lecturer Veli-Matti Ulvinen
SCRIPTA ACADEMICA
Director Sinikka Eskelinen
OECONOMICA
Professor Jari Juga
EDITOR IN CHIEF
Professor Olli Vuolteenaho
PUBLICATIONS EDITOR
Publications Editor Kirsti Nurkkala
ISBN 978-952-62-0687-5 (Paperback)
ISBN 978-952-62-0688-2 (PDF)
ISSN 0355-323X (Print)
ISSN 1796-2242 (Online)
U N I V E R S I T AT I S O U L U E N S I S
Piia Näykki
E D I T O R S
Piia Näykki
A
B
C
D
E
F
G
O U L U E N S I S
ACTA
A C TA
E 151
UNIVERSITY OF OULU GRADUATE SCHOOL;
UNIVERSITY OF OULU,
FACULTY OF EDUCATION
E
SCIENTIAE RERUM
SOCIALIUM
ACTA UNIVERSITATIS OULUENSIS
E Scientiae Rerum Socialium 151
PIIA NÄYKKI
AFFECTIVE AND EFFECTIVE
COLLABORATIVE LEARNING
Process-oriented design studies in a teacher education
context
Academic dissertation to be presented with the assent of
the Doctoral Training Committee of Human Sciences of
the University of Oulu for public defence in Kaljusensali
(KTK112), Linnanmaa, on 19 December 2014, at 12 noon
U N I VE R S I T Y O F O U L U , O U L U 2 0 1 4
Copyright © 2014
Acta Univ. Oul. E 151, 2014
Supervised by
Professor Sanna Järvelä
Professor Paul Kirschner
Reviewed by
Professor Piet Van den Bossche
Docent Maarit Arvaja
Opponent
Professor Michael Baker
ISBN 978-952-62-0687-5 (Paperback)
ISBN 978-952-62-0688-2 (PDF)
ISSN 0355-323X (Printed)
ISSN 1796-2242 (Online)
Cover Design
Raimo Ahonen
JUVENES PRINT
TAMPERE 2014
Näykki, Piia, Affective and effective collaborative learning. Process-oriented
design studies in a teacher education context
University of Oulu Graduate School; University of Oulu, Faculty of Education
Acta Univ. Oul. E 151, 2014
University of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland
Abstract
This study explores the socio-cognitive and socio-emotional activities of teacher education
students in collaborative learning, how the students interpret their activities and how the activities
influence collaborative learning.
The study consists of two empirical studies, which are reported in four articles. The first study
focuses on knowledge co-construction in face-to-face interactions enhanced with cognitive tools
and pictorial knowledge representations. The second study explores groups’ monitoring activities
in collaborative interaction, along with challenges and socio-emotional conflicts in collaborative
learning. The data collection methods include video observations, video-stimulated recall
interviews and pre- and post-knowledge tests.
The results indicate that collaborative learning is a cognitively and emotionally challenging
learning process. The way in which group members share and develop their ideas depends on how
actively they monitor their own and each other’s evolving understanding. However, monitoring
cognitive activities as a group is only one part of effective and enjoyable learning. Troubled
interaction can create a socio-emotionally unbalanced group climate, and can endanger effective
collaborative learning unless group members are capable of regulating their emotional experiences
and expression of their emotions. Therefore, in addition to effective knowledge co-construction,
effective collaborative learning requires that group members proactively monitor their own and
each other’s shared learning activities at both cognitive and emotional levels.
The findings of this study contribute to the understanding of how individuals learn together as
a group. Methodologically, this study provides several process-oriented analysis schemas for
analysing socio-cognitive and socio-emotional activities within collaborative learning.
Practically, this study offers teachers and educational professionals ideas for the design of
collaborative learning environments.
Keywords: collaborative learning, emotions, knowledge construction, monitoring,
socially shared regulation of learning
Näykki, Piia, Tunteet ja toiminta yhteisöllisessä oppimisessa. Prosessiorientoitunut
design-tutkimus opettajaopintojen kontekstissa
Oulun yliopiston tutkijakoulu; Oulun yliopisto, Kasvatustieteiden tiedekunta
Acta Univ. Oul. E 151, 2014
Oulun yliopisto, PL 8000, 90014 Oulun yliopisto
Tiivistelmä
Väitöstutkimus tarkastelee opettajaksi opiskelevien yhteisöllistä oppimista sosiokognitiivisena ja
sosioemotionaalisena vuorovaikutusprosessina. Tutkimuskohteina ovat yhteisöllinen tiedonrakentelu, yhteisöllisen oppimisen haasteet ja ryhmän toiminta sosioemotionaalisessa konfliktitilanteessa. Yhteenvedossa tarkastellaan sitä, mitä yhteisöllisen oppimisen aikana tapahtuu, miten
opiskelijat tulkitsevat prosessinaikaisia toimintoja ja miten ne vaikuttavat oppimistilanteeseen.
Tutkimus koostuu kahdesta osatutkimuksesta, joiden tulokset on raportoitu neljässä artikkelissa. Väitöstyö on luonteeltaan design-tutkimus, jossa ilmiötä tarkastellaan aidoissa, etukäteen
pedagogisesti suunnitelluissa ja tieto- ja viestintäteknologiaa hyödyntävissä oppimistilanteissa.
Tutkimusaineisto koostuu videoiduista ryhmätilanteista, opiskelijoiden haastatteluista sekä oppimistesteistä, joilla mitattiin opiskelijoiden sisällöllistä ymmärrystä. Lisäksi sosioemotionaalisen
vuorovaikutuksen analyysia syvennettiin menetelmällä, jossa opiskelijoiden haastattelua stimuloitiin videon avulla.
Tutkimustulokset osoittavat, että tiedonrakentelun yhteinen säätely edesauttaa yhteisöllistä
oppimista. Tehokas ja mielekäs yhteisöllinen oppiminen edellyttää, että ryhmän jäsenet monitoroivat, kuinka ymmärrys tehtävästä ja sisällöstä kehittyy, kuinka mielenkiinto säilyy ja kuinka
ryhmä etenee. Kognitiivisten prosessien ohella opiskelijoiden tulee tarkkailla oppimistaan sosioemotionaalisesta näkökulmasta. Ryhmien on tärkeä arvioida ja säädellä työskentelyään siten,
että sosioemotionaalinen ilmapiiri säilyy yhteisölliselle oppimiselle suotuisana.
Väitöstutkimuksen tulokset lisäävät teoreettista ymmärrystä yhteisöllisen oppimisen mahdollisuuksista ja haasteista. Tutkimus myös edistää prosessiorientoituneiden tutkimusmenetelmien
kehittämistä. Lisäksi tulosten perusteella voidaan kehittää korkeakouluopetusta ja erityisesti
opettajankoulutusta. Tutkimukseen suunnitellut oppimisympäristöt tarjoavat konkreettisia ideoita, joilla voidaan tukea yksilöllisiä ja sosiaalisesti jaettuja oppimisprosesseja.
Asiasanat: emootiot, monitoroiminen, oppimisen jaettu säätely, tiedonrakentelu,
yhteisöllinen oppiminen
Acknowledgements
The task of conducting and finalizing a PhD study has been overwhelming and
challenging on both the cognitive and emotional levels – and it would have not
been possible without the support I have been privileged to receive. I am the most
grateful to my supervisor Professor Sanna Järvelä. During these years she has
been an inspiring mentor who has encouraged and supported me in many ways.
Sanna, thank you for this opportunity to work as a part of the LET Research Unit,
thank you for believing in me and giving me many challenging opportunities to
exceed myself. I have been fortunate to have Professor Paul Kirschner as my
second supervisor. Thank you Paul, for asking difficult and thought-provoking
questions and also pointing out the weak points in my arguments. You both are
highly respected in the field of learning sciences and I feel truly honoured to be
able to work with you.
I would like to thank Professor Michael Baker for accepting the role of
opponent at the public defence of this dissertation. I greatly appreciate Professor
Baker’s scientific work and broad expertise in the area of collaborative learning
and I therefore feel especially priviledged to have him as an opponent of my
thesis. I am deeply grateful to the two pre-examiners of this dissertation, Adjunct
Professor Maarit Arvaja from the University of Jyväskylä and Professor Piet Van
den Bossche from the University of Antwerp, for their careful work and
constructive comments, which helped me to improve my arguments in the
dissertation.
One of the main reasons why scientific work is so inspiring is that it is not a
solo work. Therefore, I want to express my gratitude to my academic colleagues
near and far – it has been a priviledge to work with all of you during these years. I
would especially like to thank Professor Päivi Häkkinen, University Research
Fellow Johanna Pöysä-Tarhonen and Professor Kati Mäkitalo-Siegl for shared
research interests and working together in the Academy of Finland research
projects SCORE and PREP21.
The LET-team, both its former and current members, I would have not
managed without you all. Thank you, from the bottom of my heart! You have
been not only great colleagues but also good friends. The mentor-members of the
team, you have been a great inspiration particularly at the beginning of this PhD
project. With your example I aimed to learn the culture of the team - to support
each other, to work as a team and for the team. To name the most closest
dissertation buddies, I would like to express my gratitude to Hanna Järvenoja,
7
Jonna Malmberg, Essi Vuopala and Niina Impiö. It has been very important for
me to be able to share with you the daily hard work and the daily laughs as well
as events in everyday life. Those are the things that made also this PhD journey
possible – and enjoyable. Especially Hanna, I wish to thank you for so many
things; it has been an inspiration to construct scientific arguments with you. In
addition to motivating discussions, you have been a very valued emotional
support during these years, thank you for everything. Jonna, thank you for acting
as an seminar opponent and helping me to formulate the final structure for this
thesis. Also, I wish to thank you Jonna for the numerous stimulating scientific
discussions. Thank you, Essi and Niina, for offering your kind support in various
issues during these years of Phd studies.
Empirical work including the design and conducting of data collection for a
PhD study is challenging. I was very fortunate to be able to work together with
several people. The first data collection was carried out in collaboration with Dr.
Jürgen Scheible from Aalto University, Media Lab Helsinki. I’m thankful to have
been able to use the mobile tool he designed in the data collection. The second
data collection was conducted in collaboration with Dr. Jari Laru, whose expertise
in designing learning activities with multiple technological tools was well
appreciated. The design was planned and implemented together, but without his
technological expertise it would not have been possible. I also want to express my
appreciation to the following persons who contributed to data collection as
research assistants or Master thesis students: Tiina Luokkanen, Paulina MelakariMustonen, and Ville Tuovinen.
This thesis was financially supported by the European Science Foundation,
the Academy of Finland, the Finnish Doctoral Programme in Education and
Learning (KASVA), the Doctoral Programme for Multidisciplinary Research on
Learning Environments (OPMON), the Finnish Cultural Foundation and the
University of Oulu. I am grateful for these grants that allowed me to conduct this
PhD study. I would also like to express my gratitude to the Faculty of Education
at the University of Oulu as well as to all the teachers and students who were
involved in the empirical studies. For language editing, proof reading and
reference checking, I wish to thank Vesa Komulainen, Jaana Isohätälä, Mari
Jauhola and Lara Schmitt as well as anonymous reviewers at Scribendi and AAC
Global.
I wish to thank my friends, family and relatives. Thank you for living through
this process with me and encouraging me forward. In particular, I wish to thank
Minna Tuomikoski and Mari Jauhola as my soul sisters, you are brilliant and
8
about pure dynamics in whatever you do, great inspirers and friends to talk to in
all aspects of life. I also wish to thank Katri and Mikko Kannelsuo, Laura and
Jussi Näykki, Hanna and Miika Uusitalo, Miia and Pentti Katermaa, Minna and
Markus Tuomikoski, Mari Jauhola and Matti Pesonen – thank you all for all the
happy moments, trips and family get-togethers during these years. Those
moments have given me the much needed breaks from academic work. My
sincerest thanks go to my parents-in-law, Eeva and Aulis Näykki, and my sisterin-law, Kaisu Christie and her family. Your great can-do attitude in life has been
very inspiring. I’m trying my best to share in the attitude “others do what they
can, but the Näykkis do what they want!” Thank you Eeva and Aulis also for
everything you have done to help our family.
My warmest thanks to my dear parents, Tuula and Heikki Tolonen, for your
love, encouragement, and for endlessly believing in me at every step of my life.
Sincerest thanks to Mom and Dad for all your support, help and care for me and
our family. My daughter Iines and my son Aatos – you bring joy, love and
meaning to my life, and you put everything in the right perspective. And finally,
from the depths of my heart, I owe my deepest gratitude to my incredible husband
Antti, thank you for all the support, love and trust in the future.
Kempele, November 2014
Piia Näykki
9
10
List of original articles
This thesis is based on the following publications, which are referred to
throughout the text by their Roman numerals:
I
Näykki P & Järvelä S (2008) How pictorial knowledge representations mediate
collaborative knowledge construction in groups. Journal of Research on Technology
in Education 40(3): 359–388.
II Näykki P, Järvenoja H, Järvelä S & Kirschner P (2014) Monitoring as a regulation
activity in higher education students’ collaborative learning – Quality and temporal
variation. Manuscript submitted for publication.
III Näykki P, Järvelä S, Kirscher P & Järvenoja H (2014) Socio-emotional conflict in
collaborative learning – A process-oriented case study in a higher education context.
International Journal of Educational Research 68: 1–14.
IV Järvelä S, Järvenoja H & Näykki P (2014) Analyzing regulation of motivation as an
individual and social process – a situated approach. In: Volet S & Vauras M (eds)
Interpersonal regulation of learning and motivation: Methodological advances.
Abingdon UK, Routledge: 170–187.
11
12
Table of contents
Abstract
Tiivistelmä
Acknowledgements
7
List of original articles
11
Table of contents
13
1 Introduction
15
2 Theoretical and methodological framework
19
2.1 Learning in social interaction .................................................................. 20
2.2 Collaborative learning ............................................................................. 23
2.2.1 Socio-cognitive activities ............................................................. 25
2.2.2 Socio-emotional activities ............................................................ 27
2.3 Self-regulated learning and socially shared regulation of learning ......... 29
2.3.1 Monitoring and regulating socio-cognitive activities in
interaction ..................................................................................... 30
2.3.2 Monitoring and regulating socio-emotional activities in
interaction ..................................................................................... 32
2.4 Situative and temporal perspective to collaborative learning.................. 35
3 Aims of the study
39
4 Methods of the study
41
4.1 Design- and case-based research ............................................................. 41
4.2 Mixed methods orientation ..................................................................... 43
4.3 Participants and instructional settings ..................................................... 44
4.3.1 Study I .......................................................................................... 45
4.3.2 Study II ......................................................................................... 46
4.4 Data collection ........................................................................................ 49
4.5 Data analysis ........................................................................................... 50
4.5.1 Video analysis............................................................................... 52
4.5.2 Interview analysis ......................................................................... 55
4.5.3 Knowledge test analysis ............................................................... 56
4.6 Evaluation of the research ....................................................................... 57
4.7 Evaluation of ethical issues ..................................................................... 59
5 An overview of the articles
61
5.1 Article I. Näykki P & Järvelä S (2008) How pictorial knowledge
representations mediate collaborative knowledge construction in
13
groups. Journal of Research on Technology in Education 40(3):
359-388. .................................................................................................. 61
5.2 Article II. Näykki P, Järvenoja H, Järvelä S & Kirschner P (2014)
Monitoring as regulation activity in higher education students’
collaborative learning – Quality and temporal variation.
Manuscript submitted for publication. .................................................... 62
5.3 Article III. Näykki P, Järvelä S, Kirschner P & Järvenoja H
(2014) Socio-emotional conflict in collaborative learning – A
process-oriented case study in a higher education context.
International Journal of Educational Research 68: 1-14. ........................ 63
5.4 Article IV Järvelä S, Järvenoja H & Näykki P (2013) Analyzing
regulation of motivation as an individual and social process - A
situated approach. In: Volet S & Vauras M (eds) Interpersonal
regulation of learning and motivation: Methodological advances.
Abingdon UK, Routledge: 170-187. ....................................................... 63
6 Main findings
65
6.1 Socio-cognitive and socio-emotional activities in collaborative
learning.................................................................................................... 66
6.2 Individual interpretations of socio-cognitive and socio-emotional
activities in collaborative learning .......................................................... 69
6.3 Influences of socio-cognitive and socio-emotional activities on
collaborative learning .............................................................................. 70
6.4 Exploiting process-oriented data for understanding group
activities, influences and interpretations ................................................. 73
7 Discussion
77
8 Conclusions and future research
81
References
85
Original articles
103
14
1
Introduction
Collaborative learning skills and an ability to take an active role in one’s own
learning, the learning of others and learning together are highlighted as success
factors in a modern society (Griffin et al. 2012). In general, individuals are
expected to master the skills of communicating, collaborating, problem solving,
and critical thinking in order to participate fully and effectively in society
(Binkley et al. 2011, OECD 2013). To develop these skills, students need their
own experience of inquiry- and collaborative-based instructional approaches to
engage in solving authentic, ill-structured and complex problems (Hmelo-Silver
2004). Across all levels of schooling, an increasing amount of attention is being
devoted to exploring how students can study and learn better in groups (HmeloSilver et al. 2013). Researchers from a variety of disciplines along with
international organizations (OECD, EU and UNESCO) and educational
organizations (ISTE and NAEP) have formulated frameworks describing the
skills necessary in the 21st century (e.g. Ananiadou & Claro 2009). From this
standpoint, one of the fundamental tasks of education is to enculturate youth into
knowledge-creating civilization and to help them to find their own place in it
(Goldman & Scardamalia 2013, Rotherham & Willingham 2009, Scardamalia &
Bereiter 2006). In light of this task, traditional educational school practice – with
its emphasis on teacher-directive knowledge transmission, where the instructor is
assumed to be the distributor of knowledge and skills – appears to be limited in
scope, if not entirely missing the point.
Collaborative learning as an instructional method is characterized as a
method of supporting the development of important knowledge creation skills,
and is increasingly being used at all levels of educational systems. In the public
discourse, the term collaborative learning is often misleadingly used to refer to
any learning activities that pairs or groups of people perform together . Within the
learning sciences, collaborative learning is defined as a special form of learning
and interaction, and the ideas of co-construction of knowledge and mutual
engagement as well as coordination are highlighted (Baker 1999, Dillenbourg et
al. 1996, Jeong & Chi 2007, Kirschner et al. 2008, Roschelle 1992). Rochelle and
Teasley’s definition (1995: 70) is one of the most well-known and used:
“collaboration is a coordinated, synchronous activity that is the result of a
continued attempt to construct and maintain a shared conception of a problem”.
Working as a group is not self-evidently successful in terms of high-level
individual learning outcomes or group-level accomplishments (Barron 2003).
15
Group activities can be challenging, and particularly problem-, project- and casebased learning scenarios as collaborative learning tasks are often complex by their
nature (Kirschner et al. 2006, Savery 2006, Wilhelm et al. 2008). In short,
productive collaboration in a group learning activity, although desired, cannot be
presumed. Collaborative learning does not take place simply by putting people
together; it requires systematic cognitive, motivational and emotional effort to be
effective (Blumenfeld et al. 2006, Boekaerts & Minnaert 2006).Therefore the
nature of collaboration itself requires high-level cognitive activities as well as
emotional sensitivity to fulfil the requirements of effective collaborative learning
(Barron 2003).
In general, socio-cognitive activities within collaborative learning are well
defined, highlighting, for example, knowledge co-construction activities (Baker
1999, Chi 2009, Dillenbourg 1999, Jeong & Chi 2007, Kirschner et al. 2008,
Roschelle 1992). However, what is still missing is a more thorough elaboration of
the emotional characteristics of collaborative learning (Linnenbrink-Garcia et al.
2011) and particularly a bridge between socio-cognitive and socio-emotional
activities within collaborative learning interaction. Understanding the socioemotional dimension in collaborative learning as situated in a social context
(Baker et al. 2013) can particularly shed light on what characterizes effective
collaborative interaction. Previous studies have acknowledged meta-level
coordination activities as one of the necessities for collaborative learning (Barron
2003, Roschelle 1992), and a growing body of literature on student-led regulation
in collaborative learning contexts has emerged in recent years (Hadwin et al.
2011, Rogat & Linnenbrink-Garcia 2011, Volet et al. 2009, Volet & Vauras 2013).
For example, studies have started to elaborate (with empirical evidence) what
kind of self-regulated, co-regulated and socially shared regulation activities are
present in collaborative learning (Hadwin et al. 2011, Järvelä & Hadwin 2013).
To date, however, little is known about the nature, function and implications of
social regulatory processes in collaborative learning. For example, can groups’
differences in regulation during collaborative learning, explain differences in the
groups’ learning process and outcomes.
This study characterizes successful learning in and through collaboration as
the sum of multidimensional cognitive, social and emotional processes at both
individual and group levels, which are effectively coordinated and regulated in
interaction between the group members. Therefore, the general objective of this
study is to combine the socio-cognitive (i.e. knowledge co-construction and
monitoring evolving understanding) and socio-emotional (monitoring emotional
16
experiences and expression of emotions) dimensions to explore collaborative
learning activities, students’ individual interpretations of their activities and the
possible influence of these activities have on collaboration and learning. To
enable this, two pedagogical interventions involving inquiry-based and
technology-supported collaborative learning situations were designed in the
authentic learning context of the educational sciences and teacher education
studies.
This study highlights collaborative learning as a multifaceted and complex
phenomenon, and thus uses multiple perspectives and methods to obtain an
understanding of collaborative interactions. Throughout the dissertation, the main
focus is on process-orientation and analysis of group- and individual-level
activities (Arvaja et al. 2007, Järvelä et al. 2013, Turner 2006). In this view, the
focus shifts from what the person (as a group member) is thinking to what the
person (within a group) is doing. By looking at activity, one can analyze when¸
how and why student groups implement certain strategies (i.e. knowledge
construction and regulation). In general, analysing the dynamic and
multidimensional process of collaborative learning is challenging and has much in
common with the Indian parable about the blind men and the elephant that
describes how they observe the elephant, each from their own point of view (see
also Hmelo-Silver 2003). That example shows how each of the men perceived
only a part of the big animal and thus could only provide a narrow and
misrepresentative description of it. This is much like in analysing collaborative
learning – as with the elephant; one needs to simultaneously take account of
several points of view and use multiple methods to understand the learning
processes of a group. The original elephant example could even be broadened by
including the elephant’s situation-specific behaviour, actions and activities (e.g.
when attacking to rescue its offspring or escaping from a hunter). The central idea
of the present work is that by combining several theoretical perspectives, research
traditions, and methodologies, one can build a more comprehensive picture of
teacher education students’ collaborative learning processes. Further, connecting
the traditions may ultimately help one create innovative pedagogies and learning
environments, which may promote active collaborative learning.
This dissertation consists of two parts: In the first part, the theoretical
foundations and methodological orientations are discussed, and it concludes with
a discussion of the main findings. The second part consists of four articles
published in or submitted to international peer-reviewed journals. These articles
report the body of the empirical results of this dissertation. These are the
17
cornerstones of this dissertation study in order to explore and understand sociocognitive and socio-emotional activities, influences and interpretations in a
collaborative learning context.
18
2
Theoretical and methodological framework
Learning in groups is viewed in this dissertation as a reciprocal and contextual
activity where cognitive processes are distributed across the knower and the
environment (Pea 1993, Resnick 1991, Salomon 1993). Cognition propagates
from mind to mind, from mind to tools (i.e. technological and other artifacts) and
from tools to mind in such a way that it extends the boundaries of the mind by
creating understanding within and between the learners (Hutchins & Clausen
1998). The focus is on the processes that take place in an extended cognitive
system, which provides a basis for the coordination of individual actions as well
as for the future communication and activity of the group (Clark & Brennan 1991,
Hutchins 1995). Knowledge is something that individuals can posses and
distribute around a variety of artifacts, and other individuals and group processes
are seen as a way to facilitate individual learning. However, extended cognitive
systems, namely, those in which there are multiple people interacting with each
other and a range of artifacts to perform an activity, have properties that differ
from the individuals that participate in them (Hutchins 1995). For example,
individuals working together on a collaborative task possess different kinds of
knowledge and so will engage in interactions that will allow them to pool their
various resources to create new knowledge and understanding while
accomplishing their tasks.
This view of distributed cognitive processing is further extended within this
study with the idea of proactive and self-regulated learners in a group context.
The efficient extended cognitive system requires the negotiation and coordination
of group members’ diverse views and understandings. Students’ adaptation to
collaborative learning situations – sharing knowledge and maintaining
coordinated activity (Roschelle & Teasley 1995) – requires strategies, selfregulation (Pintrich 2000, Zimmerman 2000), and socially shared regulation of
learning (Hadwin et al. 2011, Järvelä & Hadwin 2013), which are different from,
and often more challenging than the strategies required for individual learning
(Barron 2003, Kirschner et al. 2006). Within this type of learning activities,
students are viewed as proactive participants in educational processes, being
responsible in their own learning and also the learning of others within their
learning group/community. In other words, the framework of this dissertation is
built on a vision of proactive and self-regulated learners within a group context
(Hadwin et al. 2011, Järvelä & Hadwin 2013).
19
In addition to the cognitive processes, affective and socio-emotional
processes are highlighted in this study. In other words, this dissertation advocates
the view that a purely cognitive vision of isolated individuals, and also a sociocognitive vision of social and situated individuals, needs to be broadened to a
socio-emotional vision of feeling and acting individuals. Vygotsky emphasized
that: “The separation of the intellectual aspect of our consciousness from its
affective and volitional aspect is one of the major and fundamental shortcomings
of all traditional psychology. Thinking is thereby inevitably transformed into an
autonomous current of ideas that think themselves; it is cut off from all the
fullness of real life, impulses, interests, and real tendencies of Man who thinks.”
(Vygotsky 1997: 61). It is argued that collaboration can be beneficial for learning,
but the conditions under which it is successful are elusive and positive outcomes
are not guaranteed. Group members have a proactive role in regulating their own
learning, the learning of others and learning together in order to be effective in
cognitive proceedings, but also and in particular, in order to create socioemotionally balanced and enjoyable learning environments for all the participants
in the group (Barron 2003).
The theoretical framework of this dissertation introduces and builds on
theoretical ideas of learning in social interaction, socio-cognitive and socioemotional activities and interaction in collaborative learning situation.
2.1
Learning in social interaction
More than thirty years of learning research has placed social interaction at the
focus, arguing that knowledge is built together in a social context (Sawyer 2006).
Social interaction with learning orientation has been explored, for example, in
studies on cooperation (Johnson & Johnson 1986), peer and group learning
(O’Donnell 2006), group cognition (Stahl 2006), and collaborative learning
(Baker et al. 1999, Crook 2000, Dillenbourg 1999, Dillenbourg et al. 1996,
Roschelle & Teasley 1995). The general consensus has been reached (e.g. Sawyer
2006) that interaction is the mediating mechanism whereby specific cognitive,
motivational and emotional factors are activated that can contribute to
individuals’ learning and collaboration within groups (Blumenfeld et al. 2006,
Dai & Sternberg 2004).
Slavin (1997) presented four major theoretical perspectives as explanations
for the achievement effects of cooperative learning: motivational, social cohesion,
cognitive elaboration and developmental perspectives. Even though, cooperative
20
and collaborative learning have distinct characteristics (Dillenbourg 1999), the
same perspectives can be used for justifying achievement effects of collaborative
learning. Motivational and social cohesion perspectives are related to each other
(Slavin 1997). However, whereas motivational viewpoint stresses intrinsic and
extrinsic rewards of collaboration – students help their peers in group to learn
because it is in their own interests to do so. Social cohesion theorists, in contrast,
emphasise the idea that students help each others to learn because they care about
the group. Both of these perspectives emphasises primarily motivational rather
than cognitive explanations for the effectiveness of collaborative learning. The
positive effects of social interaction on learning have also been explained by the
notion that peer interaction particularly stimulates the elaboration of knowledge,
and thus promotes individuals’ learning (van Boxtel et al. 2000). For example,
Webb (1989) found that the students who gained the most from cooperative
activities were those who provided elaborated explanations to others. Last
theoretical explanation in the Slavin’s list (1997) is developmental viewpoint.
Both major traditions of developmental psychology, the Vygotskyan and the
Piagetian, have substantially contributed to the theory of collaborative learning
and a brief review of their theoretical basis demonstrates some of the
developmental factors that explain productive social interaction.
The historical roots of learning in social interaction are often traced to
Vygotsky’s ideas of the ‘zone of proximal development’ (1978) and Piaget’s
construct of ‘socio-cognitive conflict’ (1985). The former is the centre of a sociocultural theoretical framework, where social interaction is characterized as
fundamental for the development of individuals as more knowledgeable person
scaffolds the other to learn. The latter is often used as a basis for socio-cognitive
orientation, and social interaction is viewed as important for making the
discrepancies in an individual’s understanding visible. Among Piaget’s key
assumption was that a child is situated in an environment and strives to make
sense of that environment. This means that learning occurs when individuals aim
to hold a consistent conception of their world and accommodate cognitive
structures to better represent the context. Piaget’s famous statement that, “one
comes to know the world through knowing oneself” (cited in von Glasersfeld
1982: 613), emphasizes his view that while knowledge involves descriptions of an
external world, it also invariably involves an interaction between the knower and
the known. According to neo-Piagetians (Doise & Mugny 1984) the sociocognitive conflict is one of the central mechanisms in learning interaction (Kruger
1993) and a core mechanism for the cognitive growth of individuals (Buchs et al.
21
2004, Doise & Mugny 1984). It includes both a cognitive component and a social
component, and it is characterized as a productive process of negotiation that
reaches out of the participants’ comfort zones and uncovers diversity in opinions
and identity within social interaction.
More recently, Decuyper and colleagues (2010) approached effective social
interaction from a team study perspective. In their meta-analysis, constructive
conflict was advocated as a basis for effective learning. Socio-cognitive and
socio-constructive conflict can both be defined as arising from similar branches,
as both concepts are used to define cognitive conflict processes in effective group
interaction. Substantial attention has been paid to understanding the impact of
socio-cognitive and/or constructive conflict on the processes and outcomes of
work teams (Jehn 1997, Jehn & Mannix 2001). Studies have shown that sociocognitive/constructive conflicts can be positively related to team outcomes by
encouraging confidence among the team due to a greater understanding of the
issues (De Dreu & Weingart 2003). This can be comprehended so that greater
understanding can be reached by group members’ arguing over different
perspectives, understandings and ideas. Increased confidence can further enhance
group cohesion and member commitment (Jehn & Mannix 2001). According to
van den Bossche and colleagues (2006), it is not the occurrence of conflicts that
facilitates learning and performance; rather, it is the effort expended on
elaborating different viewpoints made visible through the conflict. That is also
one of the main arguments of this thesis study. However, as group members are
challenged out of their comfort zones, these socio-cognitive conflicts are likely to
give rise also to socio-emotional conflicts or relational conflicts (Darnon et al.
2006, De Dreu & Weingart 2003, Sommet et al. 2012), which can be destructive
for effective interaction and learning, as these conflicts can also create detrimental
off-task disagreement within the group (Garcia-Prieto et al. 2003).
Socio-emotional conflict is characterized in this study as an interaction that
involves frustration and personality clashes within the group and which is
negatively related to group cohesion, commitment, satisfaction and performance
(De Dreu & Weingart 2003, Jehn & Mannix 2001). A socio-emotional approach
to collaborative interaction is one of the focal themes in this dissertation, and
thus, it will be examined more thoroughly in sections 2.2.2 in activities point of
view and 2.3.2. in regulation of activities viewpoint.
22
2.2
Collaborative learning
The study of group learning began long before studies of collaborative learning or
computer-supported collaborative learning (CSCL). Research over small groups
has a long history, for example within social psychology. To differentiate
collaborative learning from its earlier investigations of group learning, it is useful
to follow Dillenbourg’s (1999) distinction between cooperative and collaborative
learning. The often cited difference between these concepts is in the division of
labour, where the cooperative form of learning interaction is characterized as the
division of sub-tasks and responsibilities, whereas collaborative learning entails
shared learning activities (Roschelle & Teasley 1995).
Collaborative learning as a special form of learning and interaction has been
theoretically operationalized, for example, using terms of knowledge acquisition
versus participation (Sfard 1998), knowledge building (Scardamalia & Bereiter
2003), knowledge creation (Paavola et al. 2004) and as a process supported by
technology (i.e. CSCL as discussed in Stahl 2006, Stahl et al. 2006). All these
approaches and descriptive metaphors aim to target active and dynamic processes
within the group – and the community (Crook 2000). Bereiter (2002) and Bereiter
and Scardamalia (2006) differentiated knowledge-building discourse from other
types of discourse by advocating three principles: a commitment to progress, a
commitment to seek common understanding, and a commitment to expand the
base of accepted facts. The emphasis is on individuals’ taking agency over their
shared activities to generate questions and produce explanations to improve and
advance their own knowledge and understanding, as well as the knowledge of
others’ within a group/community. Even though this dissertation acknowledges
the above described principles, the concept that is implemented in this study is
knowledge co-construction instead of knowledge-building or knowledge creation,
to emphasize the cognitive knowledge construction processes (Baker 1999, Chi
2009, Jeong & Chi 2007, Kirschner et al. 2008, O’Donnell & King 1999,
Shirouzu et al. 2002, Webb et al. 1995) and to differentiate from socio-cultural
orientation and theoretical discussions of knowledge-building communities and
communities of practice (e.g. Lave & Wenger 1991).
Collaborative learning based upon the socio-cognitive paradigm advocates
students’ involvement in a process of knowledge construction, which in its most
successful forms will result in deep learning and understanding, and will enhance
conceptual change of individuals (Bereiter 2002, Bruffee 1993, Crook 2000,
Dochy et al. 2003). The power of collaborative learning is in bringing together
23
people with different experiences, values and knowledge to solve complex and
undefined problems (Light et al. 1994, Van den Bossche et al. 2006). In effective
collaboration, students engage in shared knowledge construction, coordinate
different perspectives, commit to joint goals, and evaluate together their collective
activities (Blumenfeld et al. 2006, Clark & Schaefer 1989, Roschelle & Teasley
1995). At its best, a collaborative group is able to create something that exceeds
what any individual could achieve alone.
The effectiveness of collaborative learning is best evaluated by the
participants of learning activity themselves. When the participants in the
interaction are satisfied with the quality of the process and the outcomes of their
collaboration, the collaborative learning can be seen as effective for them.
However, some general assumptions can be offered about the group
characteristics and processes likely to lead to effective (and ineffective) processes
and products of collaborative learning. This dissertation highlights the contextual
and situative process of collaboration as being more important than group
members’ individual characteristics for effective collaborative learning (Crook
2000, Greeno 2006, Järvelä et al. 2013). However, following the socio-cognitive
research tradition, personal knowledge, skills, interests and other characteristics
individuals’ bring to the collective activity have a meaning for effective
collaborative interaction. For example, groups in which members share the same
intention of achieving deep learning and conceptual change are considered to be
more effective than groups where the learning intention and goals vary greatly
(Benware & Deci 1984, Blumenfeld et al. 2006, Darnon et al. 2006, Salomon &
Globerson 1989).
The initial goal of collaborative learning research was to discover the
circumstances in which collaborative learning was more effective than learning
alone (Dillenbourg et al. 1996). For that reason, the researchers controlled for
several independent variables, including the size and composition of the group,
the nature of the task and the communication media used for learning and
interaction. However, these variables interacted in ways that made it impossible to
establish causal links between the conditions and the effects of collaboration. This
preceded a shift from determining the individual characteristics and external
conditions to determining the interpersonal interactions that occur, the conditions
under which they occur and the effects of these interactions (i.e. from the
condition paradigm to the interactions paradigm; see Dillenbourg et al. 1996).
Recent research has increasingly turned its focus on the processes that take
place – in both individual learners and in the group – during collaborative
24
learning (Arrow et al. 2004, Arvaja et al. 2007, Barron 2003, Reimann 2009 ,
Salomon & Perkins 1998, Strijbos & Fischer 2007, Weinberger et al. 2007). The
group itself has become the unit of analysis and the focus has shifted to more
emergent, socially constructed properties of the interaction (Greeno 2006).
Specific interaction processes have been explored that can increase the
effectiveness of groups, for example the monitoring of group interactions (Hare &
O’Neill 2000, Janssen et al. 2012; Sangin et al. 2011), efficient decision-making
strategies, and members showing commitment toward the task and group
(Williams et al. 2006).
Successful collaboration does not happen as frequently as might be expected.
It usually requires time and effort to accomplish a good collaborative group that
can engage deeply in shared learning processes (Fransen et al. 2013). Empirical
studies have shown that, while members of a group may cooperate, the group
itself, as a collective entity, does not always follow the mutually shared cognitive
and social processes of collaboration (Barron 2003, Järvelä & Häkkinen 2002). In
the next sections, the interaction processes in successful and less-successful
collaborative learning, with socio-cognitive and socio-emotional orientation, will
be presented.
2.2.1 Socio-cognitive activities
Collaborative learning situations offer learners opportunities to knowledge coconstruction as they call for sharing, questioning and justifying one’s own ideas
and understanding, and those of others (Chi 2009, Dillenbourg 1999, Roschelle
1992). In general, interaction among learners is the key element in learning. The
premise underlying this is that when learners externalize and articulate their
unformed, still-developing understanding together, they lean more effectively
(Baker 1999, Chi 2009, Shirouzu et al. 2002, Van der Linden et al. 2000).
The theoretical ideas of knowledge co-construction in this dissertation follow
Roschelle’s (1992) notion of convergence; group members construct shared
meanings by monitoring the degree to which they understand each other’s
thinking, extend the ideas of others, acknowledge divergent interpretations, and
resolve inconsistencies between ideas proposed (see also Fischer & Mandl 2005,
Kirschner et al. 2008, Schirouzu et al. 2002, van Boxtel et al. 2000, Weinberger et
al. 2007). Knowledge co-construction has been differentiated from information
sharing processes. The former represents high-level interactive processes where
understanding and ideas are emphasized, whereas the latter is about pooling
25
together different pieces of information. In other words, co-construction refers to
processes where group members aim to extend each other’s understanding and to
process their own as well as others’ ideas further (Baker 1995 and 1999, van
Boxtel et al. 2000, Webb et al. 1995). Furthermore, individual’s participation to
the collaborative learning activities, is theoretically explained in this dissertation
with the term of spiral of causality – “a given level of individual development
allows participation in certain social interactions which produce new individual
states which, in turn, make possible more sophisticated social interaction”
(Dillenbourg et al. 1996: 3, see also Cohen 1994, Dawes & Sams 2004, Littleton
& Miell 2004, O’Donnel & O’Kelly 1994).
One way of describing socio-cognitive interactions in collaborative learning
is to assess the degree of elaboration of information provided by one learner to
another (i.e. the continuum from providing the correct answer to providing a
detailed explanation). Prior research has, for example, introduced three potential
elaboration mechanisms enhancing learning: self-directed explaining (Chi 2000,
Chi et al. 1989), other-directed explaining (Ploetzner et al. 1999; Roscoe & Chi
2008), and co-elaboration or knowledge co-construction (Baker 2003, Damon
1984, Rafal 1996, Shirouzu et al. 2002). Self-directed explaining is a process
where thinking and problem-solving are directed at individuals’ own
understanding, not towards collaborating peers. Other-directed explaining means
that one or more individuals take the lead in instructing others or explaining how
to solve a learning task. Knowledge co-elaboration is defined as a process where
learners attend to each other’s ideas and understanding and generate new
knowledge and understanding through processing and extending the ideas
presented in the interaction (Forman & Kraker 1985, Hatano & Inagaki 1998,
Hogan et al. 1999, van Boxtel et al. 2000).
In general, this cognitive-elaboration perspective improves understanding of
the specific socio-cognitive activities and strategies students engage in while
collaborating in learning (Baker 2003, O’Donnell & King 1999, O’Donnell &
O’Kelly 1994). Previous studies within this framework, have shown that highorder thinking processes (e.g. asking complex questions and elaborating answers)
enhance learning outcomes (Howe et al. 2007, Veenman et al. 2005).
Highlighting complex explanations as particularly beneficial for individuals’
learning; i.e. when further evidence is provided and multiple concepts are
integrated into the explanation (Chinn et al. 2000, Roscoe & Chi 2008). Webb’s
meta-analysis (1991) showed that it is actually the explainer who benefits more
from this interaction than the person receiving the explanation. One possible
26
reason is that the explainer is more engaged in learning activities than the listener
(Chi 2009, Linn 2006, Webb et al. 1995). When people explain and elaborate
their own ideas to others, they are required to monitor their own thinking and to
transform their own knowledge into a relevant, coherent and complete form so
that others can understand it (Bargh & Schul 1980, Chi 2009). In order to be able
to prepare the externalization of an idea, the following cognitive processes need
to be activated: identifying the problem, prioritizing and reorganizing one’s own
previous knowledge of the problem, building new connections with new pieces of
information, and generating accurate ways of representing information (Bargh &
Schul 1980), as well as taking other perspectives into account (Levine et al.
1993).
During this process, students may recognize incompleteness or
misconceptions in their own understanding more easily than they would when
learning individually (Buchs et al. 2004, Forman & Cazden 1985). Listening to
others’ explanations allows students to monitor their own thinking and to
recognize possible gaps in their understanding (Goos et al. 2002, Palincsar &
Brown 1984). Furthermore, having one’s own ideas challenged and being asked
for justification may encourage students to seek new information, develop new
ideas and build new connections between pieces of information (Chi 2000,
Wittrock 1990).
2.2.2 Socio-emotional activities
Learners’ socio-cognitive processes during collaborative learning are well
researched, but a smaller amount of research has focused on the socio-emotional
processes that occur in collaborative learning; e.g., the creation and maintenance
of socio-emotional balance and wellbeing through sensitive relationships and a
sense of community (Kreijns et al. 2003). This dissertation argues that, during
collaborative interaction, cognitive processes interact with emotional (and
motivational) processes at both the individual and group levels (Blumenfeld et al.
2006, Crook 2000, Dai & Sternberg 2004,Thompson & Fine 1999). For example,
antecedent processes such as beliefs, interests, values and attitudes between
learning group participants are acknowledged as enhancing or inhibiting wellfunctioning interaction within a group (Boekaerts & Minnaert 2006, Harrington &
Fine 2006). In general, learning involves cognitive operations, but also how
learners feel in the situation; what kinds of negative or positive emotional
reactions are aroused. Therefore, socio-cognitive activities as an explanation for
27
the effectiveness of collaborative learning offers only one side of the
phenomenon, and the approach needs to be extended to include socio-emotional
activities.
Emotions are intense reactions that are usually generated by a process of
appraisal of the situation or dispositions that are transferred to the situation (Frijda
1986, Goetz et al. 2003, Lazarus 1991). The emotions that are traditionally listed
as basic are: fear, joy, anger, sadness, disgust and surprise (Ekman et al. 1972). In
a literature search on academic emotions, Pekrun and colleagues (2002) found
that learning research most often targets anxiety (particularly test anxiety).
Anxiety may increase task-irrelevant thinking, and thus reduce constructive
cognitive processes. However, anxiety is certainly not the only emotion that can
influence students’ cognitive (and motivational) processes and achievement,
especially within a group context. Students can experience a variety of emotions
in an academic setting: positive emotions such as enjoyiement, hope, pride and
relief, or negative emotions such as anger, shame, frustration and anxiety (Schutz
& Pekrun 2007).
The social context, and particularly collaborative learning – which is built on
the relationships of feeling human beings – is filled with different affective
experiences and emotional reactions (Baker et al. 2013). Many of these emotional
reactions relate to interpersonal interactions, and thus require one to consider
social norms and other people in the interaction (Dowson & McInerney 2003,
Goetz et al. 2003, Hareli & Weiner 2002, Järvenoja & Järvelä 2005). The
literature on group emotions considers the notion of emotional climate in terms of
shared convergent emotions within a group (Scherer & Tran 2001). The emotional
climate is based on group members’ appraisal of the situation. For example,
students in learning groups may consider the situational meaning as “having fun”,
“wasting time”, “passing time” or “learning together”. The meanings they
attribute to such a situation also influence their emotions, which when perceived
by others will further influence the overall emotional climate or atmosphere of the
group, as well as the group’s problem-solving activities; this in turn will
transform appraisals and emotions. More specifically, both negative and positive
affective states and emotions experienced within the group derive from multiple
sources that encompass a variety of factors, from personality differences to the
dynamics and processes created within the collaborative group (Järvenoja &
Järvelä 2009, Van den Bossche et al. 2006, Volet & Mansfield 2006).
Previous studies have elaborated the notion of positive and negative
emotional loops within learning interaction (Linnenbrink-Garcia et al. 2011). For
28
example, if students are positively attached to a group, they are more likely to do
their best to succeed in the learning activity. This creates a positive emotional
loop, where experience of success as a group can also increase (collective)
efficacy; beliefs that the group can master the task, and enjoy working together
(Gibson 1999). In contrast, a negative emotional loop can be created when group
members are not positively connected as a strong and well-functioning group
(Carless & De Paola 2000, Sargent & Sue-Chan 2001). If students do not, for
example, get along well with their learning partners and display negative
emotions towards them, they may decrease their engagement in the learning
activity, and thus create an asymmetrical relationship (Salonen et al. 2005) and
negative emotional loop (Linnenbrink-Garcia et al. 2011). The group members’
emotional reactions to the challenges will further influence and shape the group’s
emotional climate. When challenges emerge, the collaborative group encounters
them with the means they possess in that specific context and at that time. The
result can be positive, and thus lead to increased engagement and efforts in group
activities; alternatively, it can be negative and lead to disengagement and
withdrawal from group and its activities (Linnenbrink-Garcia et al. 2011).
2.3
Self-regulated learning and socially shared regulation of
learning
Decades of educational research has proven that to learn effectively, learners need
to take responsibility for their own learning and recognize their strengths and
weaknesses in various learning situations (Pintrich & de Groot 1990, Zimmerman
2002). This study is grounded on the theoretical assumption that the students in
collaborative learning situations similarly play an active, or even proactive, role
in their own learning. Furthermore, it is argued that as collaborative learning is
challenging student-led activity (Kirschner et al. 2006), effective collaborative
learning requires that students regulate their own learning, that of others, and
learning together (Volet & Vauras 2013). Within this line of reasoning, Hadwin
and Järvelä with their colleagues (Hadwin et al. 2011, Järvelä & Hadwin 2013)
have built a theoretical basis for socially shared regulation of learning (SSRL).
Their approach is grounded on and extends models’ of self-regulated learning
(SRL) (Pintrich 2000, Winne & Hadwin 1998, Zimmerman 1989), which broadly
refers to the processes through which learners control their own thoughts, feelings
and actions towards learning.
29
Traditionally, SRL has been conceptualized as an individual, cognitiveconstructive activity (e.g. Winne 1997, Winne & Hadwin 1998) where the social
context is viewed as one component of self-regulation (Bandura 1986, Schunk &
Zimmerman 2008, Zimmerman 2000). Self-regulated learners are characterized as
self-directed, reflective and well-organized, having the cognitive and
metacognitive skills, as well as the motivational beliefs and attitudes, needed to
understand, monitor and direct their own learning (Schunk & Zimmerman 2008,
Zimmerman 2002). Despite its focus on individual learners, SRL is also often
characterized as fundamentally social; individuals’ acquisition and use of learning
strategies are greatly influenced by modelling the learning activities of their peers
(Bandura 1986). Recently, it has been recognized that contextual features and
situational interpretations affect the strategies learners apply in their learning
(Järvelä et al. 2013, Järvelä & Niemivirta 2001, Zimmerman 2008). The recent
theoretical addition by Järvelä and Hadwin (2013) therefore aims to make social
elements of SSRL even more accurate and transparent. The ultimate goal of SSRL
is to explore how group members jointly co-construct and synthesize strategies,
monitoring, evaluation, goal setting, planning and beliefs toward shared outcomes
(Hadwin et al. 2011, Winne et al. 2013).
In this dissertation study these coordination and structuring activities are
defined as regulation of collaborative learning, which refers broadly to
individuals’ abilities to monitor, understand and control their own as well as
shared learning processes. These regulation activities are explored in this study
within socio-cognitive and socio-emotional dimensions, focusing particularly on
monitoring activities. In the next sections, monitoring activities are introduced in
more detail within a group context and with a cognitive and emotional
orientation. These processes are highlighted as important factors for monitoring
the thinking and emotions within group contextin order to communicate and
collaborate effectively (Jost et al. 1998; Nelson et al. 1998).
2.3.1 Monitoring and regulating socio-cognitive activities in
interaction
Several self-regulated learning models have introduced phases of SRL activities,
such as forethought phase, performance phase and self-reflection phase (Pintrich
2000, Zimmerman 1989, Winne & Hadwin 1998). All of the models created for
describing self-regulated learning presents cyclical regulation processes through
which learners proactively orient themselves for learning, monitor their own
30
progress, and evaluate how they accomplish their self-stated goals (Pintrich 2000,
Zimmerman 1989, Winne & Hadwin 1998). Monitoring as a regulation activity is,
thus, recognized as a fundamental part of SRL (e.g. Pintrich 2000) and SSRL
(Hadwin et al. 2011). In general, monitoring refers to individuals’ efforts to
observe themselves as they evaluate information about specific personal
processes or actions that affect their learning (Pintrich 2002). Monitoring informs
students about their own progress or lack of progress, and it aims to differentiate
effective and ineffective performance (Thoresen & Mahoney 1974, Winne 2011).
Monitoring fosters also reflective thinking (Bandura 1986) leading to a better
organisation of one’s knowledge, more accurate self-judgements and more
effective planning and goal setting for learning (Pintrich 2002).
How monitoring processes are enacted in group-level interactions is one of
the fundamental interests of this dissertation study. This question can be
theoretically operationalised by following and combining previous studies in
SRL/SSRL and collaborative learning research. Collaborative learning research
has approached monitoring processes through concepts such as: shared mental
models (Van den Bossche et al. 2006, Van den Bossche et al. 2011), common
ground (Baker et al. 1999; Beers et al. 2006, Clark & Brennan 1991, Mäkitalo et
al. 2002), or successful coordination of group working strategies (Barron 2003).
These theoretical constructs highlight the importance of monitoring activities for
effective collaboration; monitoring is emphasised as important for building and
maintaining shared mental models and common ground in interaction and can
actually be gathered under the umbrella term: awareness in collaborative learning
(Fransen et al. 2011, Kirschner et al. 2014, Leinonen et al. 2005, Schraw 1998).
In other words, monitoring is termed as an active process that aims to
increase different types of awareness in group interaction. To this end, a
distinction can be made (again following the collaborative learning research
branch) between group-, task- and content-related monitoring (Janssen et al.
2012, Saab 2012). In group-related monitoring, the focus is on the awareness of
group functioning and on the expected behaviours of both the group as a whole
and the group members individually and in relation to each other (Engelmann et
al. 2009). Task-related monitoring focuses on awareness regarding the materials
and strategies needed to successfully carry out the task. Furthermore, contentrelated monitoring (monitoring of understanding and monitoring for
understanding) is defined here as a specific monitoring process intended to
increase the individual’s own, other group members’ and shared understanding of
the content and topic on which they are currently working. Roscoe and Chi
31
(2008), for example, evaluated events where explaining own understanding by
using statements such as “I didn’t understand that before” were useful for making
new connections and building understanding at group level. Also, Janssen and
colleagues (2012) indicate that monitoring has positive effects on groups’
performance. Group-, task- and content related monitoring all facilitate task
execution by creating a framework that promotes common understanding within a
group (e.g. Janssen et al. 2012, Saab et al. 2013).
Despite a growing agreement that effective collaborative learning requires
monitoring activities, at both individual and group levels (Hadwin et al. 2011),
empirical evidence with this topic in the SSRL field is scarce, but growing. One
of the earliest work in the field, Iiskala with colleagues (2004) and Vauras with
colleagues (2003) illustrated that the dyads can monitor and regulate their
performance jointly. In a similar approach, Hurme and colleagues (2006) showed
different regulatory strategies and concluded that socially shared metacognition
was a differentiator that made a problem solving successful in groups. In general,
prior studies have shown that in collaborative problem-solving, groups in which
participants monitor their own and their peers’ learning and thinking processes
seem to have an advantage over groups who do not (Goos et al. 2002, DiDonato
2013, Hurme at el. 2006, Khosa & Volet 2013).
This dissertation study highlightes the specific monitoring activities that are
enacted within collaboration process (particularly in Articles II-III), differencies
between the number of monitoring activities and temporal variation in differently
performing groups (i.e. high- and low performing groups).
2.3.2 Monitoring and regulating socio-emotional activities in
interaction
This dissertation extends the theoretical ideas of regulation of socio-cognitive
activities by acknowledging the need to regulate emotional experiences and
emotional expressions as a shared activity as well (Baker et al. 2013, Järvenoja et
al. 2012). Social interaction and collaborative learning in particular, can be filled
with emotional variables. Groups are often ad-hoc compositions tackling with
challenging, open-ended tasks with limited time to be used – emotional
challenges are therefore easily arised. For example, differing viewpoints and
interpretations of situational demands and task requirements can lead to negative
emotional arousal within a group. To overcome these challenges, group members
32
are required to exercise control over their emotions to be effective at learning and
interaction (Wolters 2003).
Emotion regulation refers to the process involved in becoming aware of one’s
affective reactions and having an ability to monitor and control one’s emotional
experiences (Schutz & Davis 2000, Thompson et al. 2003). Boekaerts (2011)
defines emotion regulation as the capacity to understand one’s own emotions and
their expression and those of others, as well as the ability to modify or temper
aspects of the emotional experience (e.g. when it interferes with the group’s goals
and with social interaction). Wolters (2003) defines emotion regulation as
learners’ ability to monitor, evaluate and change the occurrence, intensity or
duration of a particular emotional experience so that productive engagement can
be maintained. An inability to increase or decrease the intensity and duration of
emotional arousal hinders performance and interpersonal relationships, whereas
the capacity to temper emotions facilitates functioning in social and academic
contexts (Boekaerts 2011). In fact, emotion regulation can be seen as a diverse set
of control processes manipulating which emotions are experienced, when and
how they are experienced, and how they are communicated.
While empirical studies of emotion regulation within the individual learning
context are well documented (e.g. Gross & Thompson 2007, Linnenbrink-Garcia
& Pekrun 2011), researchers have only recently begun to show interest in
studying emotional experiences, expression and regulation within collaborative
learning situations (Baker et al. 2013, Järvenoja & Järvelä 2009, LinnenbrinkGarcia et al. 2011). Järvenoja and Järvelä (2009) studied emotion and motivation
regulation within group contexts and found that students used social
reinforcement (e.g. positively supporting each other’s suggestions) and task
structuring (e.g. reducing off-task behaviour) to maintain collaborative group
work. Andriessen with colleagues (2013) studied variation of emotion regulation
through the roles of tension and relaxation in task progress during collaborative
learning. Linnenbrink-Garcia and colleagues (2011) also focused on exploring the
dynamic role of emotions in collaborative learning. They highlighted the fact that
the quality of group interactions initiated affect and that the relationship between
the quality of group interactions and affect is cyclical. For example, disrespectful
interaction was followed by sustained negative affect, creating a negative
interaction loop.
One challenge to studying emotional regulation in collaborative learning is
that, while there are classification systems for individual emotion regulation
strategies, there is no generally accepted system specifically for collaborative
33
learning. To this end, Boekaerts (2007), Op’t Eynde with colleagues (2007), and
Gross and Thompson (2007) have all produced classifications that can be adapted
to the collaborative learning context. Regulating emotions and maintaining focus
in the face of obstacles plays a central role in Boekaerts’ dual-processing selfregulation model (2007). Learners who feel unable to control emotionally
challenging learning situations will focus on coping with their emotions rather
than on participating in problem-solving. Op’t Eynde with colleagues (2007)
studied coping strategies that students use to regulate their emotions in a
mathematics classroom and defined problem-, task- and avoidance-focused
strategies. When confronted with a learning challenge, an avoidance-focused
strategy is used to escape from emotionally unpleasant situations at the expense of
learning and performance. A task-focused strategy is used to continue focusing on
task progression, while a problem-focused strategy targets the problems
experienced behind the emotional arousal (Boekaerts 2007, Op’t Eynde et al.
2007).
In addition to defining general emotion regulation strategies, more specific
emotion regulation strategies have been defined (Gross & Thompson 2007),
namely situation selection, situation modification, attentional deployment, reappraising the situation and response modulation. Situation selection may offer
short-term relief from experienced negative emotions, but it may not always be
possible within collaborative learning situations. Situation modification is
considered a strategy to change the nature of the situation. For example, the
expression of emotions within collaborative learning may change the type of ongoing group interaction (Rimé 2007). Attentional deployment (or attentional shift)
refers to strategically choosing the direction of attention, either to focus attention
away from the situation (distraction) or to draw attention to the emotional features
of the situation (concentration). Re-appraising the situation means reassessing the
situation, for example with respect to how one thinks about one’s own capacity to
manage the emotions the situation induces. Finally, response modulation is the
most direct type of emotion regulation, aiming to influence physiological,
experiential or behavioural responses, for example by acquiring and providing
social support. Through emotion regulation, it is possible to control the emotional
experience and expression of emotions and to enhance individuals’ capacity to
understand and modify their own and others’ emotions within collaborative
learning situations (Andriessen et al. 2013).
This study argues that a socio-emotionally well-functioning group is open in
the sense that it makes socio-emotional expression possible (Kreijns & Kirschner
34
2004). An open and secure atmosphere also makes it possible to express
contradictory ideas and points of view, increasing the possibility of sociocognitive conflicts (Buchs et al. 2004, Doise & Mugny 1984). However, a wellfunctioning group also recognizes when challenges are present, and particularly
what types of challenge the group is facing (i.e. cognitive, motivational and/or
socio-emotional) (Järvenoja & Järvelä 2009). Most importantly, a wellfunctioning group recognizes when there is a need for emotion regulation in order
to avoid challenges becoming detrimental conflicts (Darnon et al. 2006, De Dreu
& Weingart 2003, Sommet et al. 2012). To conclude, negative emotions
experienced within collaborative learning invite productive reactions and
successful emotion regulation from individuals as well as from the group as a
whole.
2.4
Situative and temporal perspective to collaborative learning
Interest in collaborative learning interaction entails a temporal, process-oriented
approach (Arrow et al. 2004, Bakeman & Gottman 1997, Reimann 2009). This
dissertation characterizes a group as a learning context that is a complex social
system, dynamic and constantly evolving; every new contribution is the result of
a previous discussion or decision which affords the group members new
possibilities to become involved, remain involved or withdraw from the group
and its activities (Mercer 2008). Sawyer’s (2003) notion of collaborative
emergence and Greeno’s (2006) situative approach of learning in activity are both
useful in guiding this dissertation’s theoretical grounding in a process-oriented
view of collaborative interactions.
Greeno (2006) introduces a programme of research that he refers to as
situative. The defining characteristics of a situative approach are that, instead of
focusing on individual learners, the main focus of analysis is on activity systems:
complex social organizations including learners, teachers, curriculum material,
software tools and the physical environment. Learning can thus be understood as
improved participation in interactive systems, in which the participation is related
to the other subjects and the material and representational systems within the
activity (Arvaja 2012, Greeno 1997). Individuals and the learning environment
are thus treated as inseparable (Brown et al. 1989). In other words, individuals are
not separate parts of their learning context; their participation to in knowledge coconstruction activities is viewed as a reciprocal activity between interacting minds
35
and contextual affordances (i.e. artefacts of the environment) (Fischer et al. 2002,
Fisher & Mandl 2005, Schwartz 1995).
The situative perspective builds on and synthesizes two large research
programs: cognitive science and interactional studies (Greeno 2006). These two
lines of research have been, until very recently, developed mainly in isolation
from one another. Research into the individual cognitive perspective has analysed
information structures but has had little to say about the interactions that people
have with each other and with technological resources. Research into the
interactional approach has analysed patterns of coordination of activity but has
had little to say about the information structures that are involved in the context of
joint activity. Following Greeno’s (2006) arguments, it is valuable to develop
mechanisms for bringing concepts and methods from the two programmes
together.
The sequential characteristics of collaborative learning consider which
actions typically follow each other and the temporal perspective illustrates
differences in different points in time in group interaction (Reimann 2009). This
dissertation highlights the fact that learning unfolds over time and that there are
temporal differences within group activities (Kapur 2011, Mercer 2008, Reimann
2009). In practice, the temporal perspective implies two points of view. First,
some activities are needed and used more often at certain points in a group’s
learning process (e.g. some activities are precursors of others, while some
activities follow naturally from others). Second, when learning proceeds, groups
become more capable of using some activities more often (e.g. as the group learns
certain processes that were impossible earlier, these processes become possible
and/or even necessary) (Arrow et al. 2004).
Previous studies have stated that group learning unfolds over time; is
dependent on the groups’ previous actions; is cumulative in that current
knowledge influences future knowledge; and has an anticipated future based on
the interpretation of past experiences (Hillyared et al. 2010, Reimann et al. 2011).
Fransen with colleagues (2013) suggested that it takes time before the group
members can function as an effective group. One of the most well-known
developmental models is Tuckman’s (1965) stages of group development –
forming, storming, norming, and performing – where the group goes through
different stages before achieving its best functionalities. Furthermore, the
importance of the early stages of group activities have been emphasized (e.g.
Kapur et al. 2008); they observed that group performance could be predicted
based on earlier phases of group discussion. Also, Fransen with colleagues (2011)
36
found that group effectiveness was conditional on building a shared
understanding of task characteristics and the group’s abilities in the early stages of
the group work.
Thus, tracking the temporal nature of socio-cognitive and socio-emotional
interaction as part of group development processes is essential for understanding
what emerges in a group, as well as possible reasons for why such things emerge
(Greeno 2006, Järvelä et al. 2013).
37
38
3
Aims of the study
The general objective of this study is to investigate collaborative learning by
combining socio-cognitive and socio-emotional dimensions. The aim is to study
knowledge co-construction processes and monitoring activities, as well as
emotional experiences and emotion regulation within the group. To enable this,
two pedagogical interventions were designed, involving inquiry-based and
technology-enhanced collaborative learning situations. The study was based on a
collaborative learning approach, which guided the setting of research questions
and the analysis of the data. Furthermore, one general aim was to find
methodological tools for exploring the process of collaboration and its intertwined
socio-cognitive and socio-emotional activities: the influences of activities and
students’ own interpretation of the activities.
Within this general framework, there were specific aims and research
questions in each individual study/article, which are reported in the original
articles. The general aims of this dissertation can be divided into two, where the
first set of aims focus on the empirical questions, and the second considers a
methodological question.
The empirical aims are as follows:
1. To explore teacher education students’ socio-cognitive (Articles I and II)
and socio-emotional (Article III) activities in collaborative learning
interaction.
2. To explore teacher education students’ individual interpretations of sociocognitive (Article I) and socio-emotional (Article III) activities.
3. To explore the influence of socio-cognitive (Article II) and socioemotional activities (Article III) on collaborative learning.
The methodological aim is as follows:
1. To combine data from different sources in order to understand group
activities, individual interpretations and influences (Articles I–IV).
39
40
4
Methods of the study
The research approach of this dissertations study combines characteristics of
design-based research (Barab 2006, Brown 1992), case studies (Yin 2009) and the
mixed methods approach (Cresswell 2003). In this study, instructional design is
developed in iterative cycles, where the first study is used in grounding the
second study. Furthermore, two empirical studies represent a case study approach,
defined as an empirical study that investigates collaborative learning within a
real-life, yet designed context.
4.1
Design- and case-based research
This dissertation study follows a design- and case-based research approach to
blending empirical educational research with a theory-driven design of learning
environments (Barab & Squire 2004, Confrey 2006, Design-Based Research
Collective 2003). The general goal of design-based research (DBR) in
collaborative learning research is to create learning activities, artifacts and
learning environments that enhance the practices of collaborative learning
(Confrey 2006). DBR is a form of interventionist research that creates and
evaluates novel conditions of learning (Schwartz et al. 2008) and introduces new
topics, new technologies and/or novel forms of interaction (Confrey 2006) in
order to understand how, when and why a pedagogical and/or technological
innovation works in a learning context over time and across contexts (Brown &
Campione 1996). The desired outcomes of DBR include a deeper theoretical
understanding of learning, but also, developed learning practices (Brown 1992,
Collins 1992, Schwartz et al. 2008). Reflection between learning theories and
findings gathered from DBR interventions can, for example, provide a lens for
understanding how theoretical claims about learning can be transformed into
effective teaching and studying practices (Design-Based Research Collective
2003). Furthermore, Cobb with colleagues (2003) highlight the complexity of
educational systems; a learning ecology involves multiple elements, such as
learning tasks or problems, the tools and materials, the norms of discourse and
participation, and the practical means by which teachers can orchestrate relations
between these elements. Therefore, at a general level, the designed context can be
conceptualized as interacting systems rather than separate activities (Cobb et al.
2003).
41
This class of research methods is also referred to as design research, design
experiment, or design-based research methods. Allan Collins (1992) originally
used the term design experiments to differentiate between natural science and
design science. Collins (1992: 16) wrote, “a design science education must
determine how different designs of learning environments contribute to learning,
cooperation, motivation, etc.” Ann Brown’s article (also from 1992) has been
influential on the development of the design-based research approach. Brown
(1992) specified, for example, that there is a need for rich data sources and mixed
methods in design experiments. Since the 1990s, many respected educational
researchers have promoted a design methodology for educational research that
addresses complex problems (Anderson & Shattuck 2012).
Overall, this kind of research work rejects the view that research is conducted
in laboratory or experimental settings and only later exported to classrooms.
Instead, the intention is to capture the complexity of the authentic learning
situation (Brown 1992, Greeno 2006). One fundamental characteristic of the
design process, as defined by Barab and Squire (2004), states that the design
process allows the researcher to move beyond simply understanding the world as
it is, and involves working to change it in useful ways with the broader goal of
examining how changes influence learning and practice. Collins with colleagues
(2004) posited major differences between traditional psychological methods and
the design-experiment methodology in terms of location of the research,
complexity of variables, unfolding of procedures, reporting of findings and role
of participants (see Table 1, modified from Barab 2006: 157), and these have also
guided the design approach developed within this study. Whereas, psychology
experiments are often conducted within laboratory context with a few dependent
variables, aims the DBR approach to study the phenomenon in real-world
learning environments with multiple types of dependent variables (Collins et al.
2004). The other difference that can be mentioned is that within the DBR setting
not all the variables of interest are known in advance, in contrary the interests
may change or transform during the research. Therefore, also research procedure
is flexible and evolving.
42
Table 1. Differences between design-based research and psychology experiments.
DBR
Psychology experiment
Location
Authentic learning environments
Laboratory
Complexity
Multiple types of dependent
A few dependent variables
Treatment
Not all variables of interest are
A few variables that are selected in
known in advance; emergent
advance
variables
interests
Procedure
Flexible and evolving procedures
Fixed procedures
Reporting
Describing design in practice
Testing of hypotheses
Participants
Experimenter and participants are
Experimenter should not influence
active and influence the design of
the subject; and subjects do not
the research
influence the design
This dissertation study follows the main ideas of DBR research in the sense that,
in the two empirical research interventions, the context under study, including the
pedagogical and socio-technical learning environment, were designed with
multidisciplinary collaboration. In the experimental design, appropriate tools,
instructional design and practical arrangements for learning setting were carefully
designed. Furthermore, the iteration characteristics of DBR were fulfilled by
taking the previous intervention into consideration when designing the following
study.
4.2
Mixed methods orientation
The mixed methods approach has emerged as a ‘third methodological paradigm
and in general it can be regarded as a methodological orientation that combines
quantitative and qualitative methods (Creswell 2009, Johnson & Onwuegbuzie
2004). Mixed methods research has been emerging since the 1990s, establishing
itself alongside previous paradigms so that currently all three research paradigms
coexist: quantitative, qualitative, and mixed methods research (Johnson &
Onwuegbuzie 2004). The roots of mixed methods research are generally traced to
the early work of Campbell and Fiske (1959) on mixing methods (see also
Creswell 2009, Creswell & Plano Clark 2007).
The notion of triangulation is regarded as one of the most important in the
development of the mixed methods approach (Denzin 1970, Jick 1979). Denzin
(1970) introduced different types of triangulation: 1. Methodological triangulation
(the use of more than one method for gathering data); 2. Data triangulation
43
(including several data sampling strategies at different times and situations); and
3. Theoretical triangulation (the use of more than one theoretical position in
interpreting the data). The defining characteristics of the mixed methods approach
involves its use of quantitative and qualitative methods within the same research
project and a research design that clearly specifies the sequencing and priority
that is given to the quantitative and qualitative elements of data collection and
analysis (Creswell 2009, Creswell & Plano Clark 2007). Different combinations
of methods allow rich and comprehensive views to be constructed of the
phenomena under investigation (Puntambekar 2013).
This study implements a mixed methods approach, and thus, acknowledges
both positivist as well as constructivist research paradigms. The mixed methods
approach is used for exploring both the number as well as the specific
characteristics of the phenomenon under investigation. However, the emphasis in
this study is on a qualitative approach to understanding and uncovering specific
kinds of interactions and a method of quantifying is used for the purpose of
exploring the numeric differences between groups (Chi 1997, De Wever et al.
2006, Jeong 2013, Strijbos et al. 2006). Qualitative orientation is viewed as
particularly valuable for characterizing the complex nature of collaborative
emergence, and was used to create rich descriptions of the interactions, and to
analyse selected aspects relating to specific cases (Sawyer 2013). More detailed
elaboration of the implemented data analysis methods is in the chapter 4.5.
4.3
Participants and instructional settings
Both of the empirical studies (study I and II) were implemented in a higher
education context within the Department of Educational Sciences and Teacher
Education of the University of Oulu. The specific pedagogical and technological
learning environments were designed in multidisciplinary collaboration.
The assignment which were developed for these studies required students not
only to learn and apply content knowledge, but also to generate their own learning
objectives, goals and standards of working as a group. In other words, the designs
included a macro-level scripting approach (Kobbe et al. 2007, Häkkinen et al.
2010) and open-ended problems. In general, macro scripts were used as an
activity models which aimed to structure and support collaborative learning
among students whose action or interaction was (at least partially) mediated by
technology (King 2007, Kollar et al. 2006, Weinberger et al. 2005). As defined in
Kobbe and colleagues (2007) and Dillenbourg and Jermann (2007), CSCL macro44
scripts are coarse-grained scripts that follow a pedagogy-oriented approach and
emphasize the orchestration of activities. They differ from micro-scripts, which
are finer-grained scripts following a more psychological and bottom-up approach
(Kobbe et al. 2007). The groups’ working phases were designed, but how the
groups were to work together was not specified. Thus, the students needed to
make choices, plan and together regulate their own work.
The basic idea behind the socio-technical infrastructures of these two studies
was to use available tools and services. The decision was made to use the tools,
which were easily available at the time these empirical experiment were
conducted, so that the participants (as current and future teachers) could get better
ideas about how to implement available technologicies (i.e. mobile phones, blogs
and wikis) to develop the learning environment for their own teaching purposes as
well.
4.3.1 Study I
The participants in the first study (Article I) were teacher education students
doing introductory studies in educational technology (N = 13). The participants
were randomly assigned to work in four groups. The students worked with a
collaborative learning task facilitated with the mobile mind map tool and a
problem-oriented pedagogical structure (Figure 1). The mobile mind map tool is a
prototype learning tool designed by media artist Dr. Jürgen Scheible.
Student activities were structured around different phases in which they
brainstormed, explored real-life examples to visualize their thinking and used
pictures as knowledge representations to construct a group’s shared
understanding. The idea was to use pictures as knowledge representations and an
environment as a reference point for students to visualize their individual and
shared thinking processes. The task required students to think and discuss the
following topic: What kind of qualifications do studies of educational technology
give for their future working life as a teacher and/or other educational expert?
In practice, the task began with the groups constructing mind maps with
paper and pen of the described topic. The pedagogical aim of this phase was to
support grounding; the students introduced their ideas to each other and
negotiated about different ideas. In the second phase of the task, the groups were
given mobile phones and asked to do a campus area exploration to visualize their
thinking about the task with pictures. The abstract ideas were revised into more
authentic form by asking participants to go around the campus and find real life
45
examples to support their previous discussion and then further construct new
ideas. They were asked to document their findings by taking concrete, symbolic
or framed pictures with mobile phones and by adding text annotations to the
pictures. The annotated pictures were sent to a server. The aim of this phase was
to develop and transform abstract ideas to concrete form, and thus engage
students in an problem-based inquiry process (Hakkarainen 2003, Hmelo-Silver
2004). In the third phase ideas were developed further in a face-to-face group
session by constructing a mind map of the self-generated pictorial knowledge
representations and the text annotations with the Mobile mind map tool by the
computer. The tool allowed the participants to move the pictures in a computer
screen, change the size of the pictures and also connect the pictures with a line.
The final phase included reflecting on the experience with a researcher. Each
phase lasted from half an hour to one hour. The time schedule was flexible and
groups were given as much time as they needed to finish their tasks.
Fig. 1. The design of the first case study.
4.3.2 Study II
The participants in the second empirical study were adult students (N = 22)
attending a master’s programme in education at Department of Educational
Sciences and Teacher Education. All participating students had previously
obtained a bachelor’s or master’s degree in education and had teaching and/or
administrative work experience (e.g. kindergarten teacher, kindergarten principal,
46
primary school teacher, adult educator). The students participated in a compulsory
12-week course entitled “Future Scenarios and Technologies in Learning”.
The design of the second case study (Articles II and III) consisted of lectures,
collaborative face-to-face meetings, individual blog work and collaborative group
work on a wiki (Figure 2). The overall course aim was to engage students in
learning activities that would inspire them to implement new teaching practices in
their teaching work. The content of the course included major theories and
methods of technology-enhanced learning. Following the approach of progressive
inquiry (Hakkarainen 2003) and problem-based learning (Hmelo-Silver 2004), the
learning activities in the course were organized as a cyclical model that
emphasized shared expertise and collaborative work for knowledge construction
and inquiry. This was done by setting up the context and using questions,
explanations, theories and scientific information in the cycle of deepening inquiry.
In practice, the course structure included recurrent classroom, solo and
collaborative phases mediated by the use of social software tools.
Fig. 2. The design of the second case study.
The different phases of the pedagogical design are listed and explained below (see
also Figure 2).
1. Lecture. In the first phase of the course design, the students attended to
an introductory lecture on the specific topic. The aim of the lectures was
to introduce different course topics (six themes) to the students and to
support their conceptual grounding and theoretical understanding. The
themes were, in this order: learning infrastuctures, learning communities,
metacognition, self-regulated learning, learning design and social web as
a learning environment.
2. Face-to-face group work. After the each introductory lecture, the students
worked in face-to-face groups (six meetings). The aim was to negotiate
the main approaches of the lecture and to formulate a working problem
for the group, to be continued in subsequent phases. The groups were
47
advised to set their own learning objectives and to undertake an
experiment with pictorial knowledge representations using smartphones,
blogs and wikis.
3. Individual blog work (after every face-to-face session, with six topics).
Students were asked to evaluate their everyday environment to find reallife examples and case scenarios to illustrate their ideas related to their
group’s problem. Within this phase, the idea of pictorial knowledge
representation was implemented; the students used their individual blog
learning environment to share their pictorial knowledge representations
and theoretical ideas of the topic.
4. Face-to-face group work (two meetings). The aim of the group work (in
the middle of the course and at the end of the course) was to evaluate the
previous work phases (three topics at the time) and to share ideas from
their individual blog work. Students were asked to negotiate and choose
the best examples from pictorial knowledge representations to illustrate
their group’s shared ideas, which were then used as the basis for their
shared wiki work in the next phase.
5. Group wiki work (after the first blog work). The aim of this last phase of
the design was to co-construct a group’s shared wiki. In this phase, the
students were asked to use their material (ideas and pictorial knowledge
representations) from the previous phases as well as to generate new
ideas and understanding when creating the wiki. The group wikis
functioned as shared group products, which were presented to the whole
class at the end of the course.
The socio-technological design consisted of recurrent individual, group and
collective phases where students used multiple social media tools and mobile
phones to perform designed tasks (see also Laru 2012, Laru et al. 2011, Laru et
al. 2014). The mobile device was an important tool in the designed learning
environment, as the students were required to identify, or create and capture,
situated pictorial representations with mobile phones to describe their group’s
understanding and interests. The device was equipped with the ShoZu cloudbased file-sharing tool that connected the mobile phones to the Flickr image
hosting website. ShoZu offered functions to add tags, titles and descriptions
before adding photos to the Flickr photostream. Second, an individual WordPress
blog was used as a personal learning environment, where students individually
reflected further on their ideas by writing journal entries regarding the respective
pictures/videos. The Wikispaces was used as a collaboration tool and for
48
distributing resources (i.e. course curricula, lecture slides and other information)
and displaying content from Flickr (student accounts) and WordPress (the course
blog and student blogs). These above described learning tools enabled the
connections between different task phases, the students, and the content they
produced.
The author of this dissertation participated in the design of the socio-technical
infrastructure from a pedagogical perspective, but since the main responsibility
for the technical infrastructure was taken by another researcher, the data from
virtual learning situations have been reported in studies by Laru (2012) and Laru
with colleagues (2011 and 2014), and thus, have been omitted from this
dissertation.
4.4
Data collection
This dissertation study implemented multiple data sources; including videoobservation, video-stimulated recall interviews and pre- and post-knowledge test.
Overall, video data and observational methods were regarded as the most
important for the conducted studies. Observational methods were considered as
valuable as they make possible to record what students actually do, rather than
what they recall or believe they do (Bakeman & Gottman 1997, Barron et al.
2013, Jeong 2013, Jordan & Henderson 1995). Furthermore, video-recording
afforded the opportunity to combining verbal and non-verbal behaviour of
students (Whitebread et al. 2009), which was highly valuable particularly within
socio-emotional analysis of group interaction.
The data collection in case study I (Article I) included a video observation
and interviews. Altogether six hours of video data were collected (about 90
minutes per group). During the session where the mind map tool and pictures
were used, two video cameras were used to capture the students’ facial
expressions and the activity on a computer screen. The interviews were conducted
right after the given task was completed in order for the students to reflect on
their learning experience, including how they viewed the group work, the design
of the learning experiment, and the use of cognitive tools. Furthermore, the
students’ mind maps were used to illustrate the outcome of their collaborative
learning and to stimulate the recall of their learning process during the interview.
The data collection in case study II (Articles II and III) is composed of video
recordings of the groups’ face-to-face collaborative sessions (33 h of video data),
pre- and post-tests of student knowledge, and video-stimulated recall interviews
49
(Ericsson & Simon 1980, van Gog et al. 2005). The pre- and post-knowledge test
consisted of six constructed-response questions based on the key concepts of the
course. Students were asked to write a definition of the themes of the lectures.
This meant that each theme was also connected to the specific lectures and its
associated collaborative task, and thus it was used to measure the students’
learning outcomes in a particular task. The pre-tests were also used as a method to
group students such that the level of prior knowledge was equally distributed
between the groups. Furthermore, gain scores between the two tests were used to
characterize the groups’ performances, differentiating between well- and weakperforming (Article II).
Video clips were used as stimuli in the interviews. During the individual
interviews, students were first asked to describe how their group worked together,
the challenges within their group and how they dealt with those challenges,
resolved possible conflicts and regulated their group learning. Second, the
participants were shown three video clips (2–3 minutes in length) chosen by the
researcher with a research assistant. The clips portrayed what occurred just prior
to, during and just after the emergence of challenges within the group (Article
III). The participants were instructed to report retrospectively what had happened
in the group, what they had done, how they had felt during the challenge, how
they had tried to solve the challenge, and what name they would give to the
situation in the video. The data collection overview of this dissertation study is
presented in Figure 3.
Fig. 3. Overview of the data collection.
4.5
Data analysis
Throughout this study, the main focus is on analysing and understanding the
interaction processes employed within collaborative groups (Greeno 2006).
Content analysis is adapted in this study as “a research technique for making
50
replicable and valid inferences from data to their context” (Krippendorff 2004:
21, also Chi 1997 and Strijbos et al. 2006). The central idea in content analysis is
to classify interaction into descriptive content categories. Words, phrases or larger
episodes which are recognized to have similar meanings are classified into the
same category, and the occurrences in each category are counted and their
meanings are interpreted (Weber 1990, Tesch 1990).
The analysis in the empirical studies combined theory- and data-driven
coding orientation, meaning that the content of the data was analysed according to
categorizations that arose both from theoretical and empirical content (Mayring
2000). The categories will be explained in more details in sections 4.5.1, 4.5.2
and 4.5.3. The units of analysis were chosen based on the type of data and
specific research questions in each empirical study. Defining the boundaries based
on the semantic features of small group discussions was clearly challenging and
time-consuming, adding significantly to the analytical work (Strijbos et al. 2006).
For example, in Articles I–II, the unit of analysis is turns taken in speech, whereas
in Article III the episode is used as the unit of analysis. Furthermore, attempts
were made to recognize individual and group levels within the analysis
(particularly in Article II).
A summary of the methods and data analyses in this dissertation is presented
in Table 2 and explained in the next section. However, more detailed descriptions
are provided in the original articles.
Table 2. A summary of the data analysis.
Study
Participants
Focus
Data
Analysis
Study I/ Article I
Teacher education
Knowledge co-
Video data,
Qualitative content
construction
interviews
analysis, case description
Knowledge co-
Video data, pre- Qualitative content
construction and
and post-
analysis, descriptive
monitoring
knowledge test
quantitative analysis, case
Study II/ Article II
Teacher education
description
activities
Study II/ Article III
Teacher education
Qualitative content
Challenges,
Video data,
emotional
video-stimulated analysis, interaction
experiences and
recall interview
analysis, case description
Video data
Case description
conflict
Study II/ Article IV Teacher education
Methodological
development of
process-oriented
SRL/SSRL
approach
51
4.5.1 Video analysis
Video analysis is regarded as the most important part of this dissertation study
(Barron et al. 2013, Derry et al. 2010, Goldman et al. 2007). The analysis of
interaction focused on knowledge co-construction (Articles I–II), monitoring
processes as a regulation activity (Article II) and socio-emotional challenges and
emotion regulation (Article III). Figure 4 displays an overview of the video data
analysis. In the next section, the analysis schemas are introduced in detail.
Fig. 4. Overview of the video data analysis.
Phase I – Knowledge co-construction in interaction
The first analysis phase was based on identifying the level of knowledge coconstruction within each group (Article I), i.e. analysing the groups’ knowledge
level (Cannon-Bowers & Salas 2001) and transactive level activities (Teasley
1997, Weinberger & Fischer 2006) to identify how the students used each other
52
and tools as contextual resources for building on each other’s ideas (Chi 2009,
Ploetzner et al. 1999, Roscoe & Chi 2008, Shirouzu et al. 2002). The main aim of
this kind of analysis was to detect idea chains and also to explore how these ideas
were developing within the group’s interaction. Furthermore, as pictorial
knowledge representations were used to support interaction, analysis also
evaluated what kind of interaction was stimulated with pictures.
The analysis of knowledge co-construction continued in Article II, but the
focus of the analysis shifted to the more specific analysis of the levels of
questions and answers (differentiating high, average and low level). The main
idea behind this analysis was that the level of questions and answers would
describe is group performing within high- or low-level knowledge construction
(Hmelo-Silver 2003, King 1999, Webb et al. 1995). First, all of the contentrelated questions and answers were detected. These included only the interactions
in which the group members were sharing or questioning content information. In
practise, all of the speech turns where group members were asking questions or
providing answers related to the task progress or off-task activities were excluded
from the analysis. Second, the levels of questions and answers were evaluated and
speech turns were divided either into the high-level, average-level or low-level
categories. High-level question, for example, was a broad and the answer to the
question required elaboration and high-level understanding. Whereas, low-level
question did not require elaboration, merely recalling of facts and information.
Average-level question was a middle type between above described; it was not a
broad question, but required some elaboration (see further details from Article II).
Phase II – Monitoring activities
The second phase in the video data analysis focused on what kind of monitoring
activities different groups used (Article II). The coding categories and overall
coding protocol were developed in three phases. First, prior to viewing the videos,
a list of preliminary areas of interest was developed according to the stated
research questions and the developed theoretical framework. Second, the coding
protocol was developed and elaborated further after viewing the videos and
reading the transcribed group discussions. Third, the final coding categories were
formulated and tested several times. This involved reorganization and renaming
of categories, as well as specifying sub-codes and providing examples of the
specified categories. The final version of the coding protocol included four
categories: monitoring task understanding, monitoring task progress, monitoring
53
content understanding, and monitoring interests. The idea was based on the
assumption that these types of activities would describe the groups’ meta-level
functioning and cognitive regulation processes (Pintrich et al. 2000) i.e. the
cognitive processes of planning, monitoring, controlling and evaluating during
ongoing group activities (Brown 1987, Goos et al. 2002, Son & Schwartz 2002).
In terms of monitoring content understanding and monitoring interests, the
analysis was also extended to cover the specific focus of the activity, i.e. whether
the group members’ monitored their own or another group member’s evolving
content understanding. Similarly, the interest monitoring was evaluated: were the
group members monitoring their own, others’ or their shared interests? Within,
this analysis phase, the qualitatively analysed interaction data was quantified to
detect differences between well- and weak-performing groups’ number of
monitoring activities (Chi 1997, Strijbos et al. 2006) as well as temporal variation
in monitoring activities (Reimann 2009). Qualitative case examples were also
explicated to illustrate and broaden the perspective of quantified analysis.
Phase III – Socio-emotional challenges and emotion regulation
The third analysis phase explored socio-emotional challenges and emotion
regulation (Article III). During the previous analyses (for Article II; knowledge
co-construction and monitoring activities) it became obvious that groups
experienced challenges in their collaborative learning. Thus, the focus of the
dissertation was extended to cover the variety of challenges experienced as well
as the socio-emotional processes involved in group learning. In connection with
the stimulated recall interviews, one case group was chosen for in-depth
interaction analysis, as it faced more and different types of challenges in its
collaborative learning than did the other groups (Article III, see also 4.5.2 more
details of the interview analysis). Before the analysis, the granularity of the
analysis unit was determined, and the analysis approach was refined by reviewing
the video recordings and transcripts several times. A preliminary analysis
indicated that the case group experienced a severe conflict in their first face-toface task and that the phenomena investigated could best be described through
challenging interaction episodes. Thus, the analysis was conducted within longer
sequences of interaction instead of data at the speech turn level (Bakeman &
Gottman 1997).
In practice, the transcribed video data was analysed in two phases. In the first
phase, the case group’s first face-to-face task (62 min of video observation) was
54
analysed to capture socio-emotional challenges in interaction (Barron 2003, Chiu
& Khoo 2003, Salomon & Globerson 1989). Challenges were recognized based
on visible signs of arousal, tension or frustration, or on clear problems in fluent
group work. During the analysis, four challenging interaction categories were
developed: overruling interaction, undermining interaction, status-centric
interaction and normative interaction. These interaction types created tension and
a challenging atmosphere within the group, which could be observed in the video
data, and the students also reported this atmosphere and interaction problems
during the interviews.
In the second phase, the case group’s interaction was explored in detail to
detect emotion regulation strategies. Previous studies of emotion regulation were
used to develop the coding for group-level analysis. The aim was to detect the
focus of their emotion regulation: an avoidance-, task- or problem-focused
strategy (Op’t Eynde et al. 2007) and, more specifically, situation selection,
situation modification, attentional deployment, situation re-appraisal or response
modulation (Gross & Thompson 2007). The emotion regulation interaction was
also explored by combining the stimulated recall interviews. In other words, the
video data and the students’ own interpretations of group interaction offered a
broader and more thorough picture of what was going on in the group that had
experienced socio-emotional conflict.
4.5.2 Interview analysis
In both case studies, the content of the interviews was analysed (Articles I and
III). In the first study, the analysis focused on the students’ interpretations of the
collaborative activity. The focus of the analysis was to uncover meaningful
statements regarding the students’ reflections of their learning experience,
including how they viewed their group work and the cognitive tools they had
been using. Particular attention was paid to the students’ evaluations of the use of
pictures as knowledge representations.
In the second study (Article III), the students’ individual interviews were
content-analysed to determine what kinds of challenges they had experienced in
their group work. In practice, four main categories were created: cognitive,
motivational, socio-emotional and other challenges. The cognitive challenges
category included four sub-categories: different use of terminology, different task
understandings, shallow knowledge construction and lack of self-evaluation. The
motivational challenges included three sub-categories: different interests within
55
the task, different goals for the group work, and group members not being fully
committed to the group work. The socio-emotional challenges included seven
sub-categories: too-dissimilar backgrounds, overruling others’ ideas, envy of
someone’s expertise, a pronounced need to please someone, incompatible
working styles, different styles of interaction and a lack of team cohesion. Other
challenges were personal reasons (e.g. other responsibilities in life). Next, all the
interviews were coded into categories and the data was quantified to see the
possible differences among groups.
In the second study (Article III), the data from video-stimulated recall
interviews were also analysed. The aim was to place participants back in the
group situation by using videos of their previous interactions and by posing
questions related to that experience. This type of method, which takes care to help
participants to reach a state of evocation of specific situations, seems appropriate
for understanding how emotions emerge during collaborative learning (Cahour
2013). As explained above, the stimulated recall data was explored in connection
with interaction data analysis, the students’ own recall and interpretation of their
emotional experiences and emotion regulation was coded into avoidance-, task- or
problem-focused strategies (Op’t Eynde et al. 2007) and, more specifically,
situation selection, situation modification, attentional deployment, situation reappraisal or response modulation (Gross & Thompson 2007).
4.5.3 Knowledge test analysis
Knowledge pre- and post-tests were used in Article II to characterize the students’
learning gain and to identify the students’ performances based on their individual
test results in order to compare their learning results to their group activities.
Firstly, the pre-knowledge test results were used to create groups in which the
level of knowledge was equally distributed between the participants. The gain
score of pre- and post-test was used to recognize high- and low-performing
students (in terms of this specific task). Through the knowledge test, the best
performers were localized from the different groups, allowing the well- and weakperforming groups in terms of the learning test results to be identified. The wellperforming group was recognized as the group that had the highest number of
well-performing students within it. In a similar manner, the weakest group was
recognized as the group that had the least number of good performers within the
group. The groups were, thus, divided into three categories – well-, average- and
56
weak-performing groups – based on their learning gain. This categorization was
used as the basis for further interaction analysis.
In practice, three independent researchers (including the author) developed a
criterion and marked the knowledge tests (min = 0, max = 3). The tests were
analysed by marking points for individual answers. The researchers first
negotiated the coding rules, then independently marked all the tests, and
compared the results by calculating proportion agreement (%) as reliability
indices. The pre-test coding agreement between the all three coders was 73.5%,
and the post-test agreement was 65.9%. Any differences were negotiated until
consensus was reached.
4.6
Evaluation of the research
The three articles (Articles I–III) and a methodological book chapter (Article IV)
demonstrate a wide diversity of research topics, ranging from knowledge coconstruction to monitoring activities and socio-emotional perspectives in
collaborative learning. These required independent methodological designs. The
analysis methods implemented varied, but shared a common orientation to the
collaborative learning process and focused on the interaction.
The evaluation of reliability and validity are essential criteria in research.
Patton (2002) states that validity and reliability are two factors which should be
taken into account when designing a study, analysing the results and judging the
quality of the study. In the mixed methods and qualitative research paradigms,
terms such as credibility, neutrality, consistency and applicability are useful
constructs for evaluating study’s quality (Creswell 1998, Lincoln & Guba 1985).
When quantitative and qualitative approaches are compared, it becomes obvious
that when quantitative studies aim to explain and generalize the explanations,
qualitative and mixed methods studies aim to understand and find different
possible reasons (Stenbacka 2001). This means that design studies are, in general,
intended to create novel practices, not for efficient testing and descriptions of a
causal hypothesis about learning. Design studies are more akin to case studies and
ethnographies, in that they seek to provide levels of detail and specificity about
complex interactions over extended periods of time, rather than establishing broad
and representative patterns (Confrey 2006). In the complex group settings,
understanding, explaining and gaining predictability are not about finding
universal, immutable laws, but rather about creating models of likely frameworks
that lead to successful learning outcomes, by means of theories, materials and
57
instructional approaches. In practice, a well-conducted and consistently reported
qualitative study can help to “understand a situation that would otherwise be
enigmatic or confusing” (Eisner 1991: 58).
Therefore, as Healy and Perry (2000) emphasize, the quality of a study in
each paradigm should be judged by its own paradigm stems. Reliability in
qualitatively oriented mixed methods research will be attained when the different
phases of the research are made visible and transparent (Creswell 1998). Confrey
(2006) argued over design studies methodological rigor, by advocating three
forms of warranting evidence: 1. The investigation has been adequately
conducted and analysed. 2. The claims are justified and are subjected to
alternative interpretations. 3. The relevance of the claims to the practices of
education is explicit and feasible.
This study’s methodological orientation followed a mixed methods approach,
and thus aims to implement reliability and validity criteria based on both the
quantitative paradigm and a qualitative paradigm. The documentation was
prepared within the framework of each empirical study with the purpose of
creating a careful description of the setting, data and data analysis process. In
order to ensure the validity and reliability of the study, triangulation (Stake 2003)
was used by having multiple data sources to analysing and confirming the
findings. The triangulation in this study was performed by using several types of
data, including video observations, stimulated recall interviews and knowledge
tests, to examine the phenomena (Articles I–III). Reliability was also assured by
acknowledging the participants’ points of view; using their interpretation of the
video examples to confirm the findings of the interaction analysis (Article III)
(Gijbels et al. 2006). In the analysis phase, the reliability of the coding, or “the
degree of consistency with which instances are assigned to the same category by
different observers or by the same observer on different occasions” (Hammersley
1992: 67) was checked by an independent coder (Neuendorf 2002, Silverman
2001). Reliability was also increased by offering several data examples
throughout the different studies and by using intercoder agreement (Cohen’s
kappa) as reliability assurance for the content analyses. To conclude, in each
study, multiple sources of data and analytical methods were applied to increase
the validity of the research (Puntambekar 2013).
58
4.7
Evaluation of ethical issues
The guidelines of the National Advisory Board on Research Ethics (2012) were
followed when this dissertation study was conducted; data collection, analysis and
reporting of the results have been carried out according to these ethical standards.
All of the participants in two empirical studies were volunteers, and prior to the
experiments they were informed about the experiments and data collection. All
participants were asked to give written consent to their participation, and they had
the opportunity to refuse to participate at any time during the data collection (e.g.
one of the students declined to be interviewed). None of the participants were
given extra credit or paid money for their participation. The data collection was
implemented as part of their normal curricular studies. Privacy issues were
carefully considered, and it is not possible to identify the participants in the
research articles, as pseudonyms were used. The sources of financing were
reported in articles and in the acknowledgements section of this dissertation.
59
60
5
An overview of the articles
This dissertation constitutes four articles. Table 3 summarizes the aims of each
article, along with the main responsibility of this author in each article. Articles I–
III are empirical studies, and Article IV considers methodological approaches to
studying regulated learning in a collaborative learning context. In the following
overview, the two empirical studies reported in the four articles are briefly
described.
Table 3. Focus of the articles and responsibility of the author.
Article
Aim
Author’s responsibility
I
Characteristics of knowledge co-
1st author, responsible for empirical data collection,
construction
data analysis and theoretical grounding.
II
Characteristics of knowledge co-
1st author, responsible for empirical data collection,
construction and monitoring activities
data analysis and theoretical grounding.
III
Characteristics of challenges and
1st author, responsible for empirical data collection,
conflicts
data analysis and theoretical grounding.
IV
Methodological approach to analysing
3rd author, responsible for video data analysis and
self- and socially shared regulation of
case example, contributing to the theoretical
learning
grounding.
5.1
Article I. Näykki P & Järvelä S (2008) How pictorial knowledge
representations mediate collaborative knowledge construction
in groups. Journal of Research on Technology in Education
40(3): 359-388.
The aim of the first empirical study (Article I) was to explore knowledge coconstruction processes when cognitive tools and self-created pictorial knowledge
representations were implemented to visualize one’s own, others’ and shared
ideas and understanding. The particular focus was on how students contribute to
knowledge co-construction and use each other’s ideas and tools as contextual
affordances for their learning.
The context of the study was an educational technology course for teacher
education students’ (N = 13). The data collected for this study included transcripts
of the face-to-face group activities which were video-recorded (mind maps using
paper and pen, mind maps using the Mobile Mind Map Tool, and pictorial
knowledge representations) as well as stimulated recall interviews. This analysis
61
used qualitative content analysis and revealed knowledge- and transactive-level
activities, i.e. processing one’s own and others’ ideas and understanding further.
The results show that students were more active in processing ideas further
when pictorial knowledge representations were used. Furthermore, the results
show that with pictorial representations, students started to use different
metaphors and more vivid expressions in their interaction. The use of metaphors
was interpreted as an indicator of deeper cognitive understanding, and shared
metaphors as an indicator of deeper shared understanding. However, students also
felt that reaching shared understanding was challenging, and that the use of
pictures as knowledge representations required thorough negotiation and
explanation in order to understand each other’s thinking and ideas. The
experiment was short and it was decided to continue with the same kind of
approach, but to design a longer-term experiment and theoretically build on ideas
of socially shared regulation of learning, and to empirically explore monitoring
activities within the collaborative groups.
5.2
Article II. Näykki P, Järvenoja H, Järvelä S & Kirschner P (2014)
Monitoring as regulation activity in higher education students’
collaborative learning – Quality and temporal variation.
Manuscript submitted for publication.
This study implemented a process-oriented approach to exploring what kinds of
monitoring activities groups use, along with whether and how differently
performing groups differ in terms of the amount, quality and temporal variation of
their knowledge co-construction and monitoring activities.
Five groups of teacher education students (N = 22) were observed over a
three-month course. Video recordings (33 hours) of face-to-face group interaction
(N = 12.931 speech turns) and pre- and post-tests of students’ knowledge were
collected. The video data analysis targeted knowledge co-construction (i.e.
questions and answers) and monitoring activities (i.e. monitoring task
understanding, monitoring task interests, monitoring task progress and monitoring
content understanding).
The results show that the well-performing group monitored content
understanding significantly more often and from the very beginning of the group
work, while their weak-performing counterparts focused on monitoring task-level
activities. Furthermore, the well-performing group was active at individual as
well as group levels by monitoring their own and each other’s understanding.
62
However, it became obvious during this study that it is necessary to extend the
analysis to socio-emotional processes in collaborative learning. Monitoring
cognitive activities as a group is only one part of effective and enjoyable
(collaborative) learning, and thus the next study explored challenges and
emerging socio-emotional conflict in more details.
5.3
Article III. Näykki P, Järvelä S, Kirschner P & Järvenoja H (2014)
Socio-emotional conflict in collaborative learning – A processoriented case study in a higher education context. International
Journal of Educational Research 68: 1-14.
This study explored what kinds of cognitive, motivational and socio-emotional
challenges students experienced in their collaborative learning tasks, how those
challenges turned into socio-emotional conflict within the group, and how the
students reacted to and interpreted the conflict situation.
Face-to-face collaborative learning situations involving teacher education
students (N = 22) were video-recorded (33 hours in all) during a three-month
educational science course. Cued retrospective recall interviews (with video
stimulus) were also conducted.
The results indicate that the groups faced several challenges in their
collaborative learning and there were differences between the groups in the
amount and type of challenges. One case group was chosen for in-depth analysis
as it faced more challenges, and particularly socio-emotional ones, than the other
groups. Interaction analysis indicated overruling, status-centric, undermining and
normative interaction and that the case group failed in emotion regulation
activities, which led to serious conflict and meant that group members drifted to
avoidance-focused emotion regulation behaviour.
5.4
Article IV Järvelä S, Järvenoja H & Näykki P (2013) Analyzing
regulation of motivation as an individual and social process - A
situated approach. In: Volet S & Vauras M (eds) Interpersonal
regulation of learning and motivation: Methodological
advances. Abingdon UK, Routledge: 170-187.
The aim of this study was to explain the roles of social and contextual influences
on all phases of self-regulated learning, as well as the distinctions between selfand socially shared regulation. These aims have presumed methodological efforts,
63
since many earlier methods have concentrated on the individual perspective in
regulatory activity. This book chapter discusses how different types of data and
methods of analysis make possible situated analysis of self-regulation strategies in
groups. First, the key issues in the theoretical and conceptual grounding are
explained, followed by methodological development and review of previous
studies. Then, contextual and task-specific data collection methods are introduced
and approaches to analysing regulation “in action” by combining individual- and
group-level data are explained.
64
6
Main findings
The general objective of this dissertation was to combine socio-cognitive and
socio-emotional dimensions to explore collaborative learning activities and
individual interpretations of the activities, as well as the influences of the
activities in the context of teacher education students’ collaborative learning.
Another aim was to combine data from different sources and to exploit a processoriented approach.
The study served to expand the current literature on collaborative and socially
shared regulation of learning by combining socio-cognitive and socio-emotional
dimensions to seek effective and enjoyable collaborative learning environments.
Table 4 summarizes the main dimensions of the study. The socio-cognitive view
in this study offered a point of view of actual knowledge co-construction
processes (Articles I–II) and monitoring activities in collaborative learning
(Articles II–III). The socio-emotional dimension was viewed through the
interactional challenges experienced as well as emotional responses to emerging
conflict within interaction (Article III). Figure 5 summarizes the main lines of this
study, which are explained in the following sections. The original articles present
more specific results of the empirical questions, and next the main findings are
discussed more generally based on the stated research questions of this
dissertation study.
Table 4. The main dimensions of the study.
Dimensions
Activities
Influences
Interpretations
Socio-cognitive
Knowledge- and transactive-
Learning outcomes,
Learning performance
level (Articles I-II); Monitoring
group practices (Article
(Article I)
(Article II)
II)
Socio-emotional
Challenging interaction (Article Socio-emotional conflict
Challenges, conflict,
III); Monitoring (Article III)
emotional experience
(Article III)
(Article III)
65
Fig. 5. The main approaches of the study.
6.1
Socio-cognitive and socio-emotional activities in collaborative
learning
Many studies define collaborative learning at a rather general level as mutual
engagement or a coordinated effort to solve the problem together (e.g. Roschelle
& Teasley 1995). However, this study aimed to target more specific activities. The
findings of this study indicate three levels of socio-cognitive activities:
knowledge-, transactive- and monitoring-level activities. Knowledge-level
activities demonstrate how students contribute to collaborative learning in terms
of knowledge co-construction (Articles I and II). Transactive-level (Article I)
activities explains how students use each other’s contributions and technological
tools as a contextual affordance to construct shared understanding. Monitoringlevel (Articles II and III) indicate activities which are used to monitor and
regulate group interaction. Even though these three different types of sociocognitive activities were not explored simultaneously, one of the main argument
is that collaborative learning situations require all these levels to occur in parallel
and reciprocally.
66
This dissertation’s findings highlight monitoring activities as being
particularly essential for effective collaborative learning (Goos et al. 2002, Jost et
al. 1998, Khosa & Volet 2013). Furthermore, the results indicate a distinction
between group-, task- and content-related monitoring activities (Janssen et al.
2012, Saab 2012) and define different types and focal areas of monitoring within
group interaction: monitoring content understanding, monitoring task
understanding, monitoring interests and monitoring group’s progress (Article II),
as well as monitoring emotional experiences and emotion expression in a group
context (Article III). Prior studies have shown that monitoring is an essential
component of successful self-regulated learning (Pintrich & de Groot 1990,
Schraw 1998) as well as socially shared regulation of learning (Hadwin et al.
2011, Rogat & Linnenbrink-Garcia 2011). In general, being explicit about one’s
own understanding as well as monitoring others’ understanding makes
collaborative learning more effective and fundamentally possible (Goos et al.
2002). The findings of this study complement earlier findings by highlighting the
specific monitoring sub-processes in group interaction.
Collaborative learning situations are built based on interaction between
participants’ understandings, reflected through their previous experiences,
interests and commitment towards the task and the group. This dynamic learning
process places monitoring activities in an essential role. In order to effectively coconstruct knowledge, learners need to make their thinking visible (Collins et al.
1991), to be aware of their own understanding as well as their peers’
understanding (Nelson et al. 1998, Jost et al. 1998). In addition to socio-cognitive
activities, students need to be aware of other group members’ emotional
experiences and also to monitor and regulate their emotional expression.
The third study (Article III) extended the focus from socio-cognitive
activities to also include socio-emotional dimension of collaborative learning
(Ayoko et al. 2008). The results highlight what kinds of activities/interaction
created tension and challenges for interaction in the case group (Andriessen et al.
2013), and describe troubled interpersonal dynamics, lack of communication and
dysfunctional communication (Barron 2003). Troubled dynamics and
dysfunctional interactions were reflected in various interaction behaviours, by
overruling and undermining others’ opinions and expertise and through statuscentric and normative interaction when some group members’ expertise was
emphasized at the expense of the others (Chiu & Khoo 2003, Salomon &
Globerson 1989). Barron’s studies (2000 and 2003) show similar examples of less
successful collaborative interaction, where a group was characterized as having
67
challenges in relational space, e.g. violations of turn-taking norms, competing
claims of competence, and difficulties in gaining the floor. The main contribution
of this study is, therefore, in explaining what kinds of activities lead to socioemotional conflict, as well as how students react and try to solve the conflict
(Carcia-Prieto et al. 2003, Goetz et al. 2003). In general, this study was based on
the assumption that effective collaborative learning requires students to recognize
their learning challenges and be able to respond to challenges successfully (Gross
& John 2002, Gross & Thompson 2007, Linnenbrink-Garcia et al. 2011)
Cognitive/socio-cognitive activities within collaborative learning are well
covered and defined, but there is a lack of research showing how socio-cognitive
and socio-emotional activities are intertwined in collaborative learning
interaction. The general contribution of this study is in combining different
dimensions of collaborative learning in order to understand effective collaborative
learning interaction. The emotional reactions that are awakened in learning
situations, such as the emotional atmosphere within the learning context as well
as the interaction between learners, all contribute to the learning process (Meyer
& Turner 2006). Overall, when aiming for successful collaboration with shared
understanding, it is likely that conflicting views will emerge and put cognitive as
well as emotional pressure on individuals in a group to balance the emotional
climate to be productive in knowledge co-construction (Boekaerts & Corno 2005,
Järvenoja & Järvelä 2009). These challenges highlight the importance of
understanding how collaborative groups jointly coordinate their interactions in
ways that are productive (Summers & Volet 2010, Volet et al. 2009). When some
members of the group, for example, are left out or withdraw from the group and
its activities, they have less opportunity to engage in knowledge construction and
make task contributions, with consequences for their individual learning but also
for the group’s shared outcomes. This dissertation is built on the idea that in
collaborative learning situations, the key to effective learning is a balance
between socio-cognitive and socio-emotional activities. Within the socioemotional dimension, monitoring of the socio-emotional climate, emotional
experiences and emotional expression is needed (Baker et al. 2013, Järvelä et al.
2010). The study concludes that in order to understand what makes collaborative
learning either successful or unsuccessful, the research needs to target both sociocognitive and socio-emotional activities.
68
6.2
Individual interpretations of socio-cognitive and socioemotional activities in collaborative learning
How students interpret the learning situation and the group activities they are
engaged in can have a great influence on their current functioning as a group
within the situation, but also on their future individual interest in and approach to
collaborative learning tasks. This study revealed the students’ interpretations in
terms of the collaborative learning challenges they reported experiencing, but
also, and most importantly, how they experienced socio-emotional conflict
situations.
The results show what kind of cognitive, motivational and socio-emotional
challenges students in collaborative learning groups were experiencing (Article
III). In this study, the most significant challenge type was socio-emotional
challenges. Students described their groups’ challenges, including: a lack of
connection within the group, overruling interactions, jealousy, the need to please
someone, incompatible styles of interaction and, overall, a lack of team cohesion
(Barron 2003, Chiu & Khoo 2003, Salomon & Globerson 1989). The
motivational challenges the students experienced were: different goals and
interests for the task, and commitment problems, as not all the group members
were fully devoted to the group work (e.g. Blumenfeld et al. 1996, Järvelä et al.
2008). The least-reported issue was cognitive challenges (e.g. Kirschner et al.
2008): different uses of terminology and different task understandings, as well as
superficial conversation, were experienced as group challenge. Challenges are a
natural part of group functioning, but how the challenges are interpreted and
treated is critical for the groups’ success (Van den Bossche et al. 2006). It can be
argued that if group members are positively attached to a group, they are more
likely to overcome the challenges productively (Linnenbrink-Garcia et al. 2011).
In contrast, more negative outcomes of challenges are possible when students are
not positively connected as a strong and well-functioning group (Carless & De
Paola 2000, Sargent & Sue-Chan 2001) or when there is an apparent
asymmetrical relationship between the group members (Salonen et al. 2005).
In this study, the results indicate that the socio-emotional conflict that the
case group experienced had a great influence on the students’ learning experience;
due to the conflict they were unable to feel success as a group (Article III). For
some of the students, the pressure of the conflict created a mismatch with their
learning intentions that also lowered their on-task engagement. Furthermore,
some of the students described their emotional arousal increasing in the situation
69
and thus not being able to participate fully in the group activities. The group
members explain that they escaped the unpleasant situation and regulated their
own negative emotions by withdrawing from the group activities or by
concentrating on the secondary issues (Boekaerts 2007, Op’t Eynde et al. 2007).
Above described illustrations are examples of situational interpretations that are
related to judgments, such as whether the learning situation is threatening to
individual wellbeing, as well as what is required from the individual to handle the
situation (Boekaerts & Niemivirta 2000). Regulating emotions and maintaining
focus in the face of obstacles plays a central role in Boekaerts’ dual processing
self-regulation model (2007). Learners who feel unable to control emotionally
challenging learning situations will focus on coping with their emotions rather
than on participating in problem-solving.
Overall, this study adds empirical example to the research evidence, that
success as a group is not self-evident and the students can experience several
types of challenges during their learning in interaction. It concludes that, within
collaborative learning, situations are built based on interaction between
participants’ understandings, interpreted through their previous experiences,
interests and commitment towards the task and the group (Boekaerts & Minnaert
2006, Cahour 2013, Linnenbrink-Garcia et al. 2011). Within this dynamic
learning process, how students interpret the situation and the interaction with
others becomes essential.
6.3
Influences of socio-cognitive and socio-emotional activities on
collaborative learning
In general, this study indicates what kinds of socio-cognitive and socio-emotional
activities emerged in collaborative interaction and how the students themselves
interpreted these activities. It also discusses the influences of the activities on
student groups’ collaboration and learning. This study highlights that the
occurrence and quality of these socio-cognitive and socio-emotional activities can
be used to explain why some of the groups were successful and some were less
successful in their collaborative learning; why higher or lower learning gains
were achieved and good or weak collaborative learning practices were developed.
In the first empirical study (Article I) the activities used for processing one’s
own, others’ and shared ideas further were advocated as an indication of
successful collaboration. In other words, the results show the difference of merely
adding information to the discussion and elaborating it further (Baker 1995 and
70
1999, Webb et al. 1995). This study also highlighted that pictorial knowledge
representations functioned as an affordance for a shared reference point in group
members’ knowledge co-construction. Furthermore, pictorial knowledge
representations also encouraged the use of metaphors in interaction, and this was
regarded as an indicator of deeper cognitive understanding. Shared metaphors in
particular were interpreted as an indicator of deeper shared understanding. To
conclude, the student group as well as the cognitive tools they used for their
interaction created contextual affordances for collaborative learning.
The influence of socio-cognitive activities were also approached in the
second study (Article II), where the results identified the quality or level of
questions and answers (high, average and low) that students contributed to the
group discussion. The main findings of the study show that the well-performing
group was the most active at the knowledge level (high-level questions and highlevel answers) and also gained the best results in their knowledge test. This result
indicates that knowledge-level activities and learning performance were
intertwined. The results of the study also show the temporal variation in
knowledge co-construction and monitoring activities in differently performing
groups. Temporal analysis was implemented in order to evaluate whether groups
differed in how they collaborated at the beginning and during their tasks (Article
II). The results indicated that the well-performing group was active in high-level
cognitive activities and monitoring activities; i.e. more actively monitoring
content understanding from the beginning and throughout their group activities,
whereas their weak-performing counterparts focused on monitoring task-level
activities.
This dissertation shows that emotional balance and good group functioning is
not self-evident. Many challenges can emerge during collaborative learning even
when group members engage actively in knowledge co-construction activities (as
described in section 6.2). In the previous section, the students’ interpretation of
the challenges they experienced was explained. Here, the results of what kind of
interaction created challenges are presented, as well as the influence on group
functioning in terms of expression of emotions and regulation of emotions
(Article III). For this purpose, previous studies of individuals’ emotion regulation
(e.g. Boekaerts 2007, Op’t Eynde et al. 2007) were adapted to the group level.
Based on the interaction analysis, including verbal and non-verbal interaction, an
avoidance-, task- and problem-focused emotion regulation strategy was
recognized (Op’t Eynde et al. 2007) and, more specifically, situation selection,
situation modification, attentional deployment, situation re-appraisal and response
71
modulation (Gross & Thompson 2007). The results show how the case group’s
collaborative learning was threatened as they employed avoidance-focused
emotion regulation strategies to regulate socio-emotional challenges and restore
emotional balance within the group (Boekaerts 2007, Op’t Eynde et al. 2007). In
other words, students were unable to react successfully to the emotional arousal
and frustration; thus, a conflict situation was created.
This study contributes process-oriented research evidence of what can happen
when group members fail to satisfactorily regulate their emotional experiences
and expression of emotions, resulting in the group becoming embroiled in a
conflict situation. To conclude, this study indicates that unsolved challenges can
be detrimental to effective collaborative learning as they arouse negative
emotions, such as frustration, and move the focus away from on-task working
(Ayoko et al. 2008). The study concludes that emotion regulation is important for
creating an encouraging atmosphere and for balancing the group working
environment. Finding a balance in the expression of emotions is required to
sustain engagement in collaborative learning. In order to continue effective taskoriented collaborative learning and to avoid negative experiences, members of a
group should direct their attention to solving the challenges and conflicts as well
as reducing and regulating negative emotions (Järvenoja & Järvelä 2009,
Järvenoja et al. 2012).
This study, however, does not advocate that emotions need to be put aside in
group learning; rather, it implies that the occurrence and intensity of the
emotional reaction should be controlled (Gross & Thompson 2007). Emotional
expression can even be beneficial for learning and interaction since it enables
people to take into account others’ feelings and to modify the group’s interaction
accordingly (Boekaerts 2011, Rimé 2007). This is particularly central in
collaborative learning, where the socio-emotional atmosphere is strongly present
(Linnenbrink-Garcia & Pekrun 2011). The process of monitoring emotional
experience and balancing the group’s socio-emotional atmosphere are thus
emphasized as essential feature of a well-working group. To conclude, effective
and enjoyable collaborative learning is not about outperforming others; it is about
monitoring and directing one’s own and one’s learning partners’ understanding,
interests and feelings towards the group, task and content that makes collaborative
learning either successful or unsuccessful.
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6.4
Exploiting process-oriented data for understanding group
activities, influences and interpretations
Methodological choices in this dissertation study have been guided by the idea of
focusing on the actual learning interaction and using a process-orientated
approach (Articles I–IV). Furthermore, design-based and case study approaches
were implemented, and thus, in addition to the empirical results of the
dissertation, the pedagogical design, including ideas of implementing pictorial
knowledge representations as contextual affordances for individuals’ learning, and
also to support group members’ shared learning processes, can be treated as an
important result of the study. The analysis schemas developed in the study (for
analysis of knowledge co-construction, monitoring activities and socioemotionally challenging interaction and emotion regulation) in Articles I–III also
have methodological implications and are treated as methodological results of this
dissertation.
Collaborative learning research has, to a large extent, concentrated on
studying learning in well-arranged problem-solving tasks. However, the problem
with this kind of approach is that it simplifies the multidimensional process of
collaborative learning. This dissertation study explored collaborative learning
during two authentic courses in a teacher education context as a multifaceted
phenomenon, and thus used multiple perspectives and methods to obtain an
understanding of collaborative interactions. The study was built on the idea that,
in order to develop and design practices to support collaborative learning,
research needs to target the real interactions and learning activities students
engage in when collaborating to learn.
The methods for collecting and analysing data from authentic but designed
collaborative learning situations were developed and employed in this study. The
pedagogical and socio-technical design was developed in order to evaluate social
interaction in small-group settings in two different data collection contexts and in
four different analytical approaches. Methodologically, this dissertation followed
a mixed-methods approach, and thus all the studies (Articles I–IV) used several
types of data in the analysis, combining qualitative orientation with quantifying
the interaction processes and learning gain of the students. Video-recorded
interaction data was the most important type of data for this study, but it was also
complemented with pre- and post- knowledge tests and stimulated recall
interviews (video clips and pictorial knowledge representations as a stimulus).
The interviews and knowledge tests complemented the process data with group
73
members’ individual interpretations and learning gain results. Interview data was
particularly important in Article III, where students’ own interpretations of their
group’s challenges and their emotion regulation in a socio-emotionally
challenging situation were evaluated. In other words, the study combined
interaction analysis and interview analysis to gain a more comprehensive view of
what students in groups do and what they say they do (Gijbels et al. 2006).
Particularly within the topic of socio-emotional challenges and emotion
regulation, this approach was regarded as highly useful. Therefore, in addition to
explicating what happens in group interaction, this study offers students’ own
points of view and interpretations of their activities and also, what the learning
results of their activities are.
The type of case study approach implemented in this dissertation study can
fill a gap in the research on socio-cognitive and socio-emotional processes in
collaborative learning and provide an explanation of how situations develop in
interactions between group members (Greeno 2006). Several analyses of the
video recordings enriched the understanding of group interaction as a moment-bymoment process (Barron et al. 2013). The advantages of in-depth interaction
analysis were found particularly in evaluating how interaction emerges and how
group members’ contributions were contingent on others. The process-oriented
approach in this dissertation argues over three assumptions: 1. Students’ learning
processes within a group vary across time and settings, and thus, generalization is
not reliable or sought. 2. The features of the context are unstable and have
different influences on participants in the context. 3. The direction of influence
between learners and context is multidirectional. Thus, the conclusion within this
study is to treat individuals and contexts in conjunction (Greeno 1998, Harrington
& Fine 2006, Järvelä et al. 2013).
The methodological contribution of this study is related to process-oriented,
contextual and temporal approaches to collaborative learning. The actual
collaborative interaction was targeted in all of the empirical studies (Articles I–
III). The process-oriented view captured effective knowledge co-construction
activities but also dysfunctional interaction. To date, collaborative learning
research has been criticized for its “coding and counting approach” (e.g. Arvaja
2012, Hmelo-Silver & Bromme 2007). Counting the number of turns that students
take in a discussion and whether students’ participation is on-task or off-task has
been regarded as an too narrow approach to provide an understanding of the
process of collaboration. The aggregation of data – grouping utterances by types
or codes rather than approaching them sequentially – ignores the complexity of
74
the relationships between the utterances and actions. The operationalization and
quantifying interactional phenomena risks reducing rich relationships into
categories that fail to capture the group processes and practices (Stahl 2006). The
analysis schemas of this dissertation offer ideas of how to move on from a coding
and counting approach to target the process of continuing each other’s ideas
rather than purely concentrating on the single ideas contributed to the group
discussion. The results of this dissertation study showed, for example, the
transactive level of knowledge co-construction (Article I; Teasley 1997,
Weinberger & Fischer 2006) and interactional challenges (Article III).
Today, the discussion has moved from process-oriented methods to
considering how temporal and sequential data can inform the collaborative
learning process (Molenaar & Järvelä 2014, Puntambekar 2013). In the study, the
temporal aspect was evaluated by comparing temporal processes of well- and
weak-performing groups’ monitoring activities (Article II). Within collaborative
learning, a temporal approach can be particularly valuable as it explains the time
and order of specific learning activities (e.g. Reimann 2009). Reimann with
colleagues (2011) state that group learning is dependent on the groups’ previous
actions; it is cumulative in that current knowledge influences future knowledge
and has an anticipated future based on the interpretation of past experiences.
Thus, following the development of a group’s interaction processes can also
inform the stage of the group’s functioning.
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76
7
Discussion
The group as a learning context is dynamic and constantly evolving; every new
contribution is a new potential opportunity for group members to get involved in
or withdraw from the group and its activities. Furthermore, all group situations
are different as group members bring their prior knowledge, values and beliefs
with them to their participation and the group develops along its working. This
kind of multiple and dynamic system is a challenging target for research.
This study continues the research that highlights elaborating one’s own
thinking and engaging with each other’s thinking as the main characteristics of
productive small group interaction (Baker 1999, Barron 2003, Chi 2009, Webb et
al. 1995). However, it also indicates that opportunities to benefit from knowledge
co-construction may be lost if group members do not coordinate and regulate their
learning and interaction; if they do not engage in active monitoring processes
(Goos et al. 2002, Grau & Whitebread 2012, Pintrich et al. 2000). Uncoordinated
conversations are characterized as students repeating their own ideas and
positions, ignoring and rejecting others’ suggestions without elaboration, and
interrupting and talking over others (Barron 2000). Thus, it can be concluded that
successful collaborative learning requires proactive regulation from the group
members; knowledge co-construction processes need to be activated, monitored,
evaluated and sustained proactively (Volet et al. 2009).
Previous studies have indicated that student-led and inquiry-based activities
are often very challenging (e.g. Kirschner et al. 2006) and effective collaboration
skills are not self-evident (Dawes & Sams 2004, Littleton & Häkkinen 1999,
Littleton & Miell 2004). In general, learning something new as a group requires
challenging oneself, which also increases the possibility of cognitive and socioemotional conflicts (Ayoko et al. 2008, Buchs et al. 2004, De Dreu & Weingart
2003, Garcia-Prieto et al. 2003). Within collaborative learning, and particularly
with problem-oriented tasks, students may not even have a clear vision of what
they are expected to accomplish individually and as a group. They are thus faced
with a complex system of multidirectional influences between contextual
meaning, emotions, collaboration and learning. It is quite clear that every learning
situation is challenging, but particularly group contexts orient affective states that
affect what kinds of actions students take and the relationships they together
create. The mark of successful collaborative learning in this study is activities that
are intended to coordinate and balance both socio-cognitive and socio-emotional
processes; emotional experiences as well as expression of emotions.
77
In practical terms, the results of this study can be used to assist in designing
instructional practices for collaborative learning. This dissertation study
introduced an unbalanced and poorly functioning group. The results of their
failures in collaboration and troubled interaction can be transformed into
designing and implementing instructional support to overcome challenges within
collaborative learning. For example, monitoring practices in general, related to
content and task understanding as well as evaluating interests and group progress,
could be highlighted in instructing and supporting effective collaborative
learning. Furthermore, the instructional approach can be extended from cognitive
orientation to acknowledging the importance of emotional monitoring and
regulation of emotional expression during collaborative interaction.
One of the practical ideas behind this study has been the effort to design a
learning environment that affords pre-service teachers new kinds of learning and
interaction possibilities. The fundamental thinking behind this approach is that, in
order to be able to transform schools to better correspond the requirements of the
21st century, future teachers need a modern pedagogical understanding and
methods, based on their own experiences (Binkley et al. 2011, Griffin et al.
2012). Within the case studies in this dissertation, the participants were offered an
experience of technology-enhanced collaborative learning activities that may
stimulate participants’ future (or current) working life. In other words, this study’s
practical implications are new ideas and understanding for the teachers and other
educational professionals of the skills needed in 21st-century learning (i.e.
collaboration and regulation skills) and how to support those skills in teaching.
It is especially critical to apply collaborative learning, as well as self- and
socially shared regulation of learning, in teacher education. To succeed in the
knowledge society, learners and knowledge workers need to – more often and
more effectively – combine their expertise and ideas in various collaborative
situations, to solve problems and to create new information and knowledge. Both
formal training settings and everyday learning environments require the use of
social skills and the ability to commit to coordinated work with co-learners or
colleagues. The other practical implication of the study is related to the design of
learning environments. This study followed a design-based research orientation
and, in addition to exploring group’s learning, the overall learning environment
was designed in multidisciplinary collaboration. From a practical perspective, this
study highlights how recurrent individual, group and collective phases where
students used multiple social media tools and mobile phones can be used to
support collaborative learning. However, the detailed analysis, results and
78
discussion of technology-enhanced learning were excluded from the study, since
these are reported elsewhere (Laru 2012, Laru et al. 2011, Laru et al. 2014).
The limitations of the research conducted need to be acknowledged.
Foremost, the relatively small number of subjects in the research did not provide
generalized information of collaborative interaction. However, the advantage of
this study was that it was conducted in an authentic learning context and offered
an in-depth view of group interactions. The second limitation of the study is
related to the studying of socio-emotional activities in collaborative interaction;
experienced affective movements may be masked and are not necessarily socially
expressed. More specifically, the limitation of this study is that real access to the
socio-emotionally experiences was dependent on the students’ willingness to
engage in disclosure of their experienced emotions. Indicators of challenges and
emotional experiences were detected based on the group’s interaction and the
group members’ interpretation of the video stimulus. Thus, with this multiple
approach the aim was to increase the reliability of the analysis related to
emotional processes. The written report, in the form of research articles and
thesis, is not sufficient to describe the participants’ emotional experiences and
expression of emotions. They lack the non-verbal expression that is observed on a
video recording of the interaction, such as gestures, posture and movements. It is
obvious that when I, as a researcher, look at transcripts of these interactions, I can
hear the voices, the enthusiasms about a great new idea or the frustration of not
understanding the task, the anger in the voice of an offended participant, and the
general engagement and disengagement of the students. But a reader without
access to the video cannot. An attempt was made to overcome this limitation by
offering several data examples, particularly in Article III. The broad perspective
of this dissertation study can also set limitations; when several dimensions are
approached simultaneously, the clarity of the study may suffer. However, the
broad approach was chosen with the intention of offering a more illustrative view
of collaborative learning.
79
80
8
Conclusions and future research
This study’s approach to collaborative learning is broad; it covers socio-cognitive
and socio-emotional dimensions in knowledge co-construction and regulation
within collaborative learning. Furthermore, three perspectives are implemented:
activities, interpretations and influences. On one hand, the study explores what
makes a collaborative group successful; however, in the last study this approach
was reversed by pointing out factors that created socio-emotional conflict within
the group and endangered successful collaborative learning. Overall, it is
concluded that, as collaborative learning is wide-ranging, a broad focus is needed
to explore cognitive as well as emotional perspectives to contribute to a
discussion of what makes collaborative learning successful or unsuccessful. Each
of these perspectives also offers ideas for future research.
Most modern authors agree that there has been a bias towards cognitive
approaches in learning research, at the expense of socio-emotional or affective
activities (e.g. Jones & Issroff 2005). Within collaborative learning research,
previous studies have explored social and relational processes to some extent (e.g.
Barron 2003, Kreijns et al. 2003). However, only recently has collaborative
learning research started to acknowledge socio-emotional processes, emotional
experiences and expression and regulation of emotions as an important
explanation for effective collaborative learning (Baker et al. 2013). Further
research is, however, needed to explore the socio-emotional characteristics of
well- and poorly functioning groups.
The study shows how concepts which were initially developed for individual
learning settings can also help to develop a socio-emotional perspective in
collaborative learning research. In general, the impact of socio-emotional
dispositions on thought processes is well established, but mostly in individual
experimental settings (Linnenbrink-Garcia & Pekrun 2011, Schutz & Pekrun
2007). The psychological literature on emotions is often subject-centred and not
group-oriented, concentrating mostly on individuals’ methods of appraising and
coping in the critical situation for their own sake (Lazarus 1991). More work
needs to be done on complex learning situations and ecologically valid contexts in
order to gain a deeper understanding of these interactions between socioemotional processes and complex cognitive activities in collaborative learning
situations.
This study acknowledges that the emergence of an emotion is a subjective
phenomenon which depends on individual characteristics, but also about
81
interaction with the specific situation and the meaning that the situation has for a
person (Boekaerts & Minnaert 2006, Scherer & Tran 2001). Therefore, future
research should aim to target both aspects: individual characteristics as well as
contextual influence on experiencing strong emotions in collaborative learning
situations. For example, knowing something about the actual challenges and
emotional experiences learners confront (individually or collectively) in
collaborative arrangements can provide information about what they are trying to
accomplish as they experiment with or negotiate task understanding, shared goals,
and cognitive or motivational strategies (Hadwin et al. 2011, Järvelä et al. 2014).
In the future, emotional experiences and expression should also be analysed in
terms of different dimensions: for example, valency (positive and negative
tonality), degree of arousal (level of intensity), triggering event, level of bodily
reaction, degree of control, consciousness, and duration (Cahour 2013). These
processes, based on the students’ interaction but also in connection to their
physical reactions, could signal how the dynamics of collaborative learning are
managed and why collaborative learning sometimes leads to undesirable
outcomes (Barron 2003).
To conclude, the need for an emotional balance within group is clear when
one considers the cognitive, motivational and emotional challenges of
collaborative learning (Järvenoja & Järvelä 2009, Kirschner et al. 2006).
However, more research needs to be carried out in order to understand how
emotions circulate and interact with knowledge elaboration in natural settings. In
the future, more specific theoretical models are needed that explicitly address the
interplay between individual cognition, emotion and motivation with
interpersonal communication and regulation, in the context of a collaborative
learning environment.
The search for socio-cognitive and socio-emotional balance, not only for
oneself but also for the persons with whom one interacts, or for the group itself,
seems to be an essential process that should be a target for collaborative learning
research. This ability to be in contact with others’ cognition and emotions is a
primary source of intersubjectivity. How individuals as a group learn together;
share and develop their ideas, and especially how they monitor their evolving
understanding and emotional reactions, were the cornerstones of this study.
Personally, participation in the improvisation theatre exercise was an eye-opener.
In that world, it is not about how well one can act in a front of the audience; it is
about how to make one’s acting partner(s) shine! This example transfers well to
collaborative learning – collaborative learning is not about outperforming others,
82
it is about monitoring and directing one’s own and one’s learning partners’
understanding, interests and feelings towards the group, task and content that
makes collaborative learning either successful or unsuccessful. This study is an
example that a group is more than the sum of its parts; something unique and
often unpredictable is created between and within interacting minds.
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References
Anderson T & Shattuck J (2012) Design-based research: A Decade of progress in
education research? Educational Researcher 41: 16–25.
Ananiadou K & Claro M (2009) 21st century skills and competences for new millennium
learners in OECD countries. (OECD Education Working Papers No. 41). Paris, OECD.
Andriessen J, Pardijs M & Baker M (2013) Getting on and getting along. Tension in the
development of collaborations. In: Baker M, Andriessen J & Järvelä S (eds) Affective
Learning Together – Social and emotional dimensions of collaborative learning. New
York NY, Routledge: 205–230.
Arrow H, Poole MS, Henry KB, Wheelan S & Moreland R (2004) Time, change and
development: The temporal perspective on groups. Small Group Research 35(1): 73–105.
Arvaja M (2012) Personal and shared experiences as resources for meaning making in a
philosophy of science course. International Journal of Computer-Supported Collaborative
Learning 7(1): 85–108.
Arvaja M, Salovaara H, Häkkinen P & Järvelä S (2007) Combining individual and group-level
perspectives for studying collaborative knowledge construction in context. Learning and
Instruction 17: 448–459.
Ayoko OB, Callan VJ & Härtel CE (2008) The Influence of Team Emotional Intelligence
Climate on Conflict and Team Members’ Reactions to Conflict. Small Group Research
39(2): 121–149.
Bakeman R & Gottman JM (1997) Observing Interaction: An introduction to sequential
analysis. New York NY, Cambridge University Press.
Baker M (1995) Negotiation in collaborative problem-solving dialogues. In: Beun RJ,
Baker M & Reiner M (eds) Dialogue and Instruction. Modeling interaction in
intelligent tutoring systems. Berlin, Springer-Verlag: 39–55.
Baker M (1999) Argumentation and constructive interaction. In: Coirier P & Andriessen J
(eds), Foundations of Argumentative Text Processing. Amsterdam, University of
Amsterdam Press: 179–202.
Baker M (2002) Forms of cooperation in dyadic problem-solving. In: Salenbier P &
Benchekroun H (eds) Cooperation and Complexity. Paris, Hermès: 587–620.
Baker M (2003) Computer-mediated argumentative interactions for the co-elaboration of
scientific notions. In: Andriessen J, Baker M & Suthers D (eds) Arguing to Learn:
Confronting cognitions in computer-supported collaborative learning environments.
Dordrecht, Kluwer: 47–78.
Baker M, Andriessen J & Järvelä S (2013) Affective Learning Together – Social and
emotional dimensions of collaborative learning. Abingdon UK, Routledge.
Baker M, Hansen T, Joiner R & Traum D (1999) The Role of Grounding in Collaborative
Learning Tasks. In: Dillenbourg P (ed) Collaborative Learning: Cognitive and
Computational Approaches. Amsterdam, Pergamon: 31–64.
Bandura A (1986) Social Foundations of Thought and Action: A Social Cognitive Theory.
Englewood Cliffs NJ, Prentice-Hall.
85
Barab S (2006) Design-Based Research. A methodological toolkit for the learning
scientist. In: Sawyer RK (ed) The Cambridge Handbook of the Learning Sciences.
New York NY, Cambridge University Press: 153–169.
Barab S & Squire K (2004) Design-Based research: Putting a stake in the ground. The
Journal of the Learning Sciences 13(1): 1–14.
Bargh JA & Schul Y (1980) On the cognitive benefits of teaching. Journal of Educational
Psychology 72: 593–604.
Barron B (2000) Achieving coordination in collaborative problem-solving groups. The
Journal of the Learning Sciences 9(4): 403–436.
Barron B (2003) When smart groups fail. The Journal of the Learning Sciences 12(3):
307–359.
Barron BJ, Pea R & Engle RE (2013) Advancing understanding of collaborative learning
with data derived from video records. In: Hmelo-Silver CE, Chinn CA, Chan CKK &
O’Donnell A (eds) The International Handbook of Collaborative Learning. New York
NY, Routledge: 203–219.
Beers PJ, Boshuizen HPA., Kirschner, PA & Gijselaers WH (2006) Common ground, complex
problems and decision making. Group Decision and Negotiation 15(6): 529–556.
Benware CA & Deci EL (1984) Quality of learning with an active versus passive motivational
set. American Educational Research Journal 21(4): 755–765.
Bereiter C (2002) Education and Mind in the Knowledge Age. Mahwah NJ, Lawrence Erlbaum
Associates.
Bereiter C & Scardamalia M (2006) Education for the knowledge age: Design-centered models
of teaching and instruction. In: Alexander PA & Winne PH (eds) Handbook of
Educational Psychology (2nd ed.). Mahwah NJ, Lawrence Erlbaum Associates: 695–713.
Binkley M, Erstad O, Herman J, Raizen S, Ripley M & Rumble M (2011) Defining 21st
century skills. In: Griffin P, McGaw B & Care E (eds) Assessment and Teaching of 21stCentury Skills. New York NY, Springer: 17–66.
Blumenfeld P, Kempler T & Krajcik J (2006) Motivation and cognitive engagement in
learning environments. In: Sawyer RK (ed) The Cambridge Handbook of the Learning
Sciences. Cambridge UK, Cambridge University Press: 475–488.
Blumenfeld P, Marx R, Soloway E & Krajcik J (1996) Learning with peers: From small
group co-operation to collaborative communities. Educational Researcher 25(8): 37–
40.
Boekaerts M (2007) Understanding Students’ affective processes in the classroom. In
Schutz P, Pekrun R & Phye G (eds) Emotion in Education. San Diego CA, Academic
Press: 37–56.
Boekaerts M (2011) Emotions, emotion regulation, and self-regulated learning. In:
Zimmerman BJ & Schunk DH (eds) Handbook of Self-Regulation of Learning and
Performance. New York NY, Routledge: 408–425.
Boekaerts M & Corno L (2005) Self-regulation in the classroom: A perspective on
assessment and intervention. Applied Psychology 54(2): 199–231.
Boekaerts M & Minnaert A (2006) Affective and Motivational Outcomes of Working in
Collaborative Groups. Educational Psychology 26(2): 187–208.
86
Boekaerts M & Niemivirta M (2000) Self-regulation in learning: Finding a balance
between learning- and ego-protective goals. In: Boekaerts M, Pintrich PR & Zeidner
M (eds) Handbook of Self-Regulation. San Diego CA, Academic Press: 417–450.
Brown AL (1987) Metacognition, executive control, self-regulation and other more
mysterious mechanisms. In: Weinert FE & Kluwe RH (eds) Metacognition,
motivation and understanding. Hillsdale NJ, Erlbaum: 65–116.
Brown AL (1992) Design experiments: Theoretical and methodological issues in creating
complex intervention in classroom settings. The Journal of the Learning Sciences,
2(2): 141–178.
Brown AL & Campione J (1996) Psychological theory and design of innovative learning
environments: On procedures, principles, and systems. In: Schauble L & Glaser R
(eds) Innovations in Learning: New environments for education. Mahwah NJ,
Lawrence Erlbaum Associates, Inc: 289–298.
Brown JS, Collins A & Duguid P (1989) Situated Cognition and the Culture of Learning.
Educational Researcher 18(1): 32–42.
Bruffee K (1993) Collaborative Learning: Higher education, interdependency, and the
authority of knowledge. Baltimore MD, Johns Hopkins University Press.
Buchs C, Butera F, Mugny G & Darnon C (2004) Conflict elaboration and cognitive
outcomes. Theory into Practice 43: 23–30.
Cahour B (2013) Emotions: characteristics, emergence and circulation in interactional
learning. In: Baker M, Andriessen J & Järvelä S (eds) Affective Learning Together –
Social and emotional dimensions of collaborative learning. Abingdon UK, Routledge:
52–70.
Campbell DT & Fiske DW (1959) Convergent and discriminant validation by the
multitrait-multimethod matrix. Psychological Bulletin 56(2): 81–105.
Cannon-Bowers JA & Salas E (2001) Reflections on shared cognition. Journal of
Organizational Behavior 22: 195–202.
Carless SA & DePaola CD (2000) The measurement of cohesion in work teams. Small
Group Research 31(1): 71–88.
Chi MTH (1997) Quantifying qualitative analyses of verbal data: A practical guide. The
Journal of the Learning Sciences 6(3): 271–315.
Chi MTH (2000) Self-explaining expository texts: The dual processes of generating inferences
and repairing mental models. In: Glaser R (ed) Advances in Instructional Psychology.
Hillsdale NJ, Lawrence Erlbaum Associates: 161–238.
Chi MTH (2009) Active-Constructive-Interactive: A Conceptual Framework for Differentiating
Learning Activities. Topics in Cognitive Science 1(1): 73–105.
Chi MTH, Bassok M, Lewis MW, Reiman P & Glaser R (1989) Self-explanations: How
students study and use examples in learning to solve problems. Cognitive Science 13:
145–182.
Chinn CA, O’Donnell AM & Jinks TS (2000) The structure of discourse in collaborative
learning. Journal of Experimental Education 69(1): 77–97.
87
Chiu MM & Khoo L (2003) Rudeness and status effect during group problem solving: Do
they bias evaluation and reduce the likelihood of correct solutions? Journal of
Educational Psychology 95: 506–523.
Clark H & Brennan S (1991) Grounding in communication. In: Resnick L, Levine J &
Teasley S (eds), Perspectives on socially shared cognition. Washington DC, American
Psychological Association: 127–149.
Clark HH & Schaefer EF (1989) Contributing to discourse. Cognitive Science 13: 259–294.
Cobb P, Confrey J, diSessa A, Lehrer R & Schauble L (2003) Design experiments in
educational research. Educational Researcher 32(1): 9–13.
Cohen EG (1994) Restructuring the classroom: conditions for productive small groups.
Review of Educational Research 64(1): 1–35.
Collins A (1992) Toward a design science of education. In: Scanlon E & O’Shea T (eds)
New Directions in Educational Technology. New York NY, Springer Verlag: 15–22.
Collins A, Brown JS & Holum A (1991) Cognitive apprenticeship: Making thinking
visible. American Educator 15(3): 6–11 & 38–46.
Collins A, Joseph D & Bielaszyc K (2004) Design research: Theoretical and
methodological issues. Journal of the Learning Sciences: 13, 15–42.
Confrey J (2006) The evolution of design studies as methodology. In: Sawyer RK (ed) The
Cambridge Handbook of the Learning Sciences. New York NY, Cambridge
University Press: 135–151.
Creswell JW (1998) Qualitative Inquiry and Research Design: Choosing Among Five
Traditions. Thousand Oaks CA, Sage Publications.
Creswell JW (2009) Research Design: Qualitative, quantitative, and mixed methods
approaches. (3rd ed.). Thousand Oaks CA, Sage Publications.
Creswell JW & Plano Clark VL (2007) Designing and Conducting Mixed Methods
research. Thousand Oaks CA, Sage Publications.
Crook C (2000) Motivation and the ecology of collaborative learning. In: Joiner R,
Littleton, Faultner KD & Miell D (eds) Rethinking Collaborative Learning. London
UK, Free Association Books: 161–178.
Dai D & Sternberg R (eds) (2004) Motivation, Emotion, and Cognition: Integrative
Perspectives on Intellectual Functioning and Development. Mahwah NJ, Lawrence
Erlbaum Associates, Inc. Publishers.
Damon W (1984) Peer education: The untapped potential. Journal of Applied
Developmental Psychology 5(4): 331–343.
Darnon C, Muller D, Schrager SM, Pannuzzo N & Butera F (2006) Mastery and
performance goals predict epistemic and relational conflict regulation. Journal of
Educational Psychology 98(4): 766–776.
Dawes L & Sams C (2004) Developing the capacity to collaborate. In: Littleton K, Miell D
& Faulkner D (eds) Learning to Collaborate, Collaborating to Learn: Understanding
and promoting educationally productive collaborative work. New York NY, Nova
Science Publishers, Inc: 95–110.
88
Decuyper S, Dochy F & Van den Bossche P (2010) Grasping the dynamic complexity of
team learning: An integrative model for effective team learning in organisations.
Educational Research Review 5(2): 111–133.
De Dreu CKW & Weingart LR (2003) Task versus relationship conflict, team performance,
and team member satisfaction: A meta-analysis. Journal of Applied Psychology 88(4):
741–749.
Denzin NK (1970) The Research Act in Sociology. Chicago IL, Aldine.Derry S, Pea R,
Barron B, Engle R, Erickson F, Goldman R, Hall R, Koschmann T, Lemke JL, Gamoran
Sherin M & Sherin B (2010) Conducting video research in the learning science: Guidance
on selection analysis, technology and ethics. Journal of Learning Sciences 19(1): 3–53.
Design-Based Research Collective (2003) Design-Based Research: An emerging paradigm for
educational inquiry. Educational Researcher 32(1): 5–8.
De Wever B, Schellens T, Valcke M & van Keer H (2006) Content analysis schemes to analyse
transcripts of online asynchronous discussion groups: a review. Computers and Education
46: 6–28.
DiDonato NC (2013) Effective self- and co-regulation in collaborative learning groups: An
analysis of how students regulate problem solving of authentic interdisciplinary tasks.
Instructional Science 41(1): 25–47.
Dillenbourg P (1999) What do you mean by ‘collaborative learning’? In: Dillenbourg P (ed)
Collaborative Learning: Cognitive and computational approaches. Oxford UK,
Elsevier: 1–19.
Dillenbourg P, Baker M, Blaye A & O’Malley C (1996) The evolution of research on
collaborative learning. In: Spada E & Reiman P (eds) Learning in Humans and
Machines: Towards an Interdisciplinary Learning Science. Oxford UK, Elsevier: 189–
211.
Dillenbourg P & Jermann P (2007) SWISH: A model for designing CSCL scripts. In:
Fischer F, Mandl H, Haake J & Kollar I (eds) Scripting Computer-Supported
Collaborative Learning – Cognitive, computational, and educational perspectives.
Computer-supported Collaborative Learning Series. New York NY, Springer: 275–
301.
Dillenbourg P, Järvelä S & Fischer F (2009) The evolution of research on computersupported collaborative learning: from design to orchestration. In: Balacheff N,
Ludvigsen S, de Jong T, Lazonder A & Barnes S (eds) Technology-Enhanced
Learning: Principles and Products. Dordrecht, Springer Science+Business Media
B. V.: 3–19.
Dillenbourg P & Tchounikine P (2007) Flexibility in macro-scripts for computer-supported
collaborative learning. Journal of Computer Assisted Learning 23(1): 1–13.
Dochy F, Segers M, Van den Bossche P & Gijbels D (2003) Effects of problem-based
learning: A meta-analysis. Learning and Instruction 13: 533–568.
Doise W & Mugny G (1984) The Social Development of the Intellect. Oxford UK,
Pergamon Press.
89
Dowson M & McInerney DM (2003) What do students say about their motivational goals?
Towards a complex and dynamic perspective on student motivation. Contemporary
Educational Psychology 28: 91–113.
Eisner EW (1991) The Enlightened Eye. New York NY, Macmillan.
Ekman P, Friesen WV & Ellsworth P (1972) Emotions in the Human Face: Guidelines for
Research and an Integration of Findings. New York NY, Pergamon Press.
Engelmann T, Dehler J, Bodemer D & Buder J (2009) Knowledge awareness in CSCL: A
psychological perspective. Computers in Human Behavior 25: 949–960.
Ericsson KA & Simon HA (1980) Verbal reports as data. Psychological Review: 87(3),
215–251.
Fischer F, Bruun J, Gräsel C & Mandl H (2002) Fostering collaborative knowledge
construction with visualization tools. Learning and Instruction 12(2): 213–232.
Fischer F & Mandl H (2005) Knowledge convergence in computer-supported collaborative
learning: The role of external representation tools. The Journal of the Learning
Sciences 14(3): 405–441.
Forman EA & Cazden CB (1985) Exploring Vygotskian perspectives in education: The
cognitive value of peer interaction. In: Wertsch JV (ed), Culture, Communication, and
Cognition: Vygotskian Perspectives. New York NY, Cambridge University Press:
323–347.
Forman EA & Kraker MJ (1985) The social origins of logic: The contributions of Piaget
and Vygotsky. In: Berkowitz MW (ed) Peer Conflict and Psychological Growth. San
Francisco CA, Jossey-Bass: 23–39.
Fransen J, Kirschner P & Erkens G (2011) Mediating team effectiveness in the context of
collaborative learning: The importance of team and task awareness. Computers in
Human Behavior 27: 1103–1113.
Fransen J, Weinberger A & Kirschner PA (2013) Team effectiveness and team development in
CSCL. Educational Psychologist 48(1): 9–24.
Frijda NH (1986) The Emotions. Cambridge UK, Cambridge University Press.
Garcia-Prieto P, Bellard E & Schneider SC (2003) Experiencing diversity, conflict, and
emotions in teams. Applied Psychology: An International Review 52(3): 413–440.
Gibson CB (1999) Do they do what they believe they can? Group efficacy and group
effectiveness across tasks and cultures. Academy of Management Journal 42(2): 138–
152.
Gijbels D, van de Watering G, Dochy F & Van den Bossche P (2006) New learning
environments and constructivism: the students' perspective. Instructional Science
34(3): 213–226.
Goetz T, Zirngibl A, Pekrun R & Hall N (2003) Emotions, learning and achievement from
an educational-psychological perspective. In: Mayring P & Rhoeneck CV (eds),
Learning Emotions: The Influence of Affective Factors on Classroom Learning.
Frankfurt am Main, Peter Lang: 9–28.
Goldman SR & Scardamalia M (2013) Managing, understanding, applying, and creating
knowledge in the information age: Next-generation challenges and opportunities.
Cognition and Instruction 31(2): 255–269.
90
Goldman R, Pea RD, Barron B & Derry S (2007) (eds) Video research in the learning sciences.
Mahwah, NJ, Lawrence Erlbaum Associates.
Goos M, Galbraith P & Renshaw P (2002) Socially mediated metacognition: Creating
collaborative zones of proximal development in small group problem solving. Educational
Studies in Mathematics 49(2): 193–223.
Grau V & Whitebread D (2012) Self and social regulation of learning during collaborative
activities in the classroom: The interplay of individual and group cognition. Learning and
Instruction 22: 401–412.
Greeno JG (1997) On claims that answer the wrong questions. Educational Researcher 26(1):
5–17.
Greeno JG (1998) The situativity of knowing, learning and research. American Psychologist
53: 5–26.
Greeno JG (2006) Learning in activity. In: Sawyer K (ed) The Cambridge Handbook of the
Learning Sciences. New York NY, Cambridge University Press: 79–95.
Griffin P, McGaw B & Care E (2012) Assessment and Teaching of 21st Century Skills. New
York, Springer.
Gross JJ & John OP (2002) Wise emotion regulation. In: Feldman Barrett L & Salovey P
(eds) The Wisdom in Feeling: Psychological processes in emotional intelligence. New
York NY, Guilford: 297–318.
Gross JJ & Thompson RA (2007) Emotion regulation: Conceptual foundations. In: Gross
JJ (ed) Handbook of Emotion Regulation. New York NY, Guilford: 3–24.
Hacker DJ (1998) Definition and empirical foundations. In: Hacker DJ, Dunlosky J &
Graesser AC (eds) Metacognition in Educational Theory and Practice. Mahwah NJ,
Erlbaum Group: 1–23.
Hadwin A, Järvelä S & Miller M (2011) Self-regulated, co-regulated and socially shared
regulation of learning. In: Zimmerman B & Schunk D (eds) Handbook of Self-Regulation
of Learning and Performance. New York NY, Routledge: 65–84.
Hakkarainen K (2003) Progressive inquiry in a computer-supported biology class. Journal of
Research in Science Teaching 40(10): 1072–1088.
Hammersley M (1992) What’s Wrong with Ethnography? Methodological Explorations.
Abingdon UK, Routledge.
Hare L & O’Neill K (2000) Effectiveness and efficiency in small academic peer groups: A case
study. Small Group Research 31: 24–53.
Hareli S & Weiner B (2002) Social emotions and personality inferences: A scaffold for a new
research direction in the study of achievement motivation. Educational Psychologist 37:
183–193.
Harrington B & Fine GA (2006) Where the Action Is: Small Groups and Recent Developments
in Sociological Theory. Small group research 37(1): 4–19.
Hatano G & Inagaki K (1998) Sharing cognition through collective comprehension activity. In:
Faulkner D, Littleton K & Woodhead M (eds) Learning Relationships in the Classroom.
Abingdon UK, Routledge: 276–292.
91
Healy M & Perry C (2000) Comprehensive criteria to judge validity and reliability of
qualitative research within the realism paradigm. Qualitative Market Research: An
International Journal 3(3): 118–26.
Hillyard C, Gillespie D & Littig P (2010) University students’ attitudes about learning in small
groups after frequent participation. Active Learning in Higher Education 11: 9–20.
Hmelo-Silver C (2003) Analyzing collaborative knowledge construction: Multiple methods for
integrated understanding. Computers & Education 41: 397–420.
Hmelo-Silver C (2004) Problem-based learning: What and how do students learn? Educational
Psychology Review 16: 235–266.
Hmelo-Silver CE & Bromme R (2007) Coding discussions and discussing coding: Research on
collaborative learning in computer-supported environments. Learning and Instruction 17:
460–464.
Hmelo-Silver CE, Chinn CA, Chan CKK & O’Donnell A (2013) (eds) The International
Handbook of Collaborative Learning. New York NY, Routledge.
Hogan K, Nastasi BK & Pressley M (1999) Discourse patterns and collaborative scientific
reasoning in peer and teacher-guided discussions. Cognition & Instruction 17(4): 379–432.
Howe C, Tolmie A, Thurston A, Topping K, Christie D, Livingston K, Jessiman E &
Donaldson C (2007) Group work in elementary science: Towards organisational principles
for supporting pupil learning. Learning and Instruction 17(5): 549–563.
Hurme T-R, Palonen T & Järvelä S (2006) Metacognition in joint discussions: an analysis of
the patterns of interaction and the metacognitive content of the networked discussions in
mathematics. Metacognition and Learning 1(2): 181–200.
Hutchins E (1995) Cognition in the Wild. MIT Press.
Hutchins E & Clausen T (1998) Distributed cognition in an airline cockpit. In: Engeström Y &
Middleton D (eds) Cognition and Communication at Work. New York NY, Cambridge
University Press: 15–34.
Häkkinen P, Arvaja M, Hämäläinen R & Pöysä J (2010) Scripting Computer-Supported
Collaborative Learning: A review of SCORE Studies. In: Ertl B (ed) E-Collaborative
Knowledge Construction: Learning from Computer-Supported and Virtual Environments.
IGI Global, New York: 180–194.
liskala T, Vauras M & Lehtinen E (2004) Socially-shared metacognition in peer learning?
Hellenic Journal of Psychology 1(2): 147–178.
Iiskala T, Vauras M, Lehtinen E & Salonen P (2011) Socially shared metacognition of dyads of
pupils in collaborative mathematical problem-solving process. Learning and Instruction
21: 379–393.
Janssen J, Erkens G, Kirschner PA & Kanselaar G (2012) Task-related and social regulation
during online collaborative learning. Metacognition and Learning 7(1): 25–43.
Jehn K (1997) A qualitative analysis of conflict types and dimensions in organizational
groups. Administrative Science Quarterly 42(3): 530–557.
Jehn KA & Mannix EA (2001) The dynamic nature of conflict: A longitudinal study of
intragroup conflict and group performance. Academy of Management Journal 44(2):
238–251.
92
Jeong H (2013) Verbal data analysis for understanding interactions. In: Hmelo-Silver C,
O’Donnell AM, Chan C & Chinn C (eds), The international handbook of collaborative
learning. London, Routledge: 168–183.
Jeong H & Chi MTH (2007) Knowledge convergence during collaborative learning.
Instructional Science, 35: 287–315.
Jick TD (1979) Mixing qualitative and quantitative methods: Triangulation in action.
Administrative Science Quarterly 24: 602–611.
Johnson DW & Johnson RT (1986) Computer-assisted cooperative learning. Educational
Technology 26: 12–18.
Johnson B & Onwuegbuzie A (2004) Mixed Methods Research: A Research Paradigm
Whose Time Has Come. Educational Researcher 33(7): 14–26.
Jones A & Issroff K (2005) Learning technologies: affective and social issues in computersupported collaborative learning. Computers and Education 44: 395–408.
Jordan B & Henderson A (1995) Interaction analysis: Foundations and practice. The
Journal of Learning Sciences 4(1): 39–103.
Jost J, Kruglanski A & Nelson T (1998) Social metacognition: An expansionist review.
Personality and Social Psychology Review 2(2): 137–154.
Järvelä S & Hadwin A (2013) New Frontiers: Regulating Learning in CSCL. Educational
Psychologist 48(1): 25–39.
Järvelä S & Häkkinen P (2002) Web-based cases in teaching and learning – the quality of
discussions and a stage of perspective taking in asynchronous communication.
Interactive Learning Environments 10(1): 1–22.
Järvelä S, Järvenoja H & Näykki P (2013) Analyzing regulation of motivation as an individual
and social process – A situated approach. In: Volet S & Vauras M (eds) Interpersonal
Regulation of Learning and Motivation: Methodological Advances. New York NY,
Routledge: 170–187.
Järvelä S, Järvenoja H & Veermans M (2008) Understanding dynamics of motivation in
socially shared learning. International Journal of educational Research 47(1): 122–135.
Järvelä S, Kirschner P, Panadero E, Malmberg J, Phielix C, Jaspers J, Koivuniemi M &
Järvenoja H (2014) Enhancing Socially Shared Regulation in Collaborative Learning
Groups: Designing for CSCL Regulation Tools. Educational Technology Research
and Development.
Järvelä S & Niemivirta M (2001) Motivation in context: Challenges and possibilities in
studying the role of motivation in new pedagogical cultures. In: Volet S & Järvelä S
(eds) Motivation in Learning Contexts: Theoretical Advances and Methodological
Implications. London UK, Pergamon/Elsevier: 105–127.
Järvelä S, Volet S & Järvenoja H (2010) Research on motivation in collaborative learning:
Moving beyond the cognitive-situative divide and combining individual and social
processes. Educational Psychologist 45: 15–27.
Järvenoja H & Järvelä S (2005) How students describe the sources of their emotional and
motivational experiences during the learning process: A qualitative approach.
Learning and Instruction 15(5): 465–480.
93
Järvenoja H & Järvelä S (2009) Emotion control in collaborative learning situations – do
students regulate emotions evoked from social challenges? British Journal of
Educational Psychology 79: 463–481.
Järvenoja H, Volet S & Järvelä S (2012) Regulation of emotions in socially challenging
learning situations: An instrument to measure the adaptive and social nature of the
regulation process. Educational Psychology 33(1): 1–28.
Kapur M (2011) Temporality matters: Advancing a method for analyzing problem-solving
processes in a computer-supported collaborative environment. International Journal of
Computer-Supported Collaborative Learning 6(1): 39–56.
Kapur M, Voiklis J & Kinzer C (2008) Sensitivities to early exchange in synchronous
computer-supported collaborative learning (CSCL) groups. Computers and Education
51(1): 54–66.
Khosa D & Volet S (2013) Promoting effective collaborative case-based learning at university:
a metacognitive intervention. Studies in Higher Education 38(6): 870-889.
King A (1999) Discourse patterns for mediating peer learning. In: O’Donnell AM & King A
(eds) Cognitive perspectives on peer learning. Mahwah NJ, Erlbaum: 87–117.
King A (2007) Scripting collaborative learning processes: A cognitive perspective. In:
Fischer F, Kollar I, Mandl H & Haake J (eds) Scripting computer-supported
collaborative learning: Cognitive, computational, and educational perspectives. New
York NY, Springer: 13–37.
Kirschner PA, Beers PJ, Boshuizen HPA & Gijselaers WH (2008) Coercing shared
knowledge in collaborative learning environments. Computers in Human Behavior 24:
403–420.
Kirschner PA, Sweller J & Clark RE (2006) Why minimal guidance during instruction
does not work: An analysis of the failure of constructivist, discovery, problem-based,
experiential, and inquiry-based teaching. Educational Psychologist 41: 75–86.
Kobbe L, Weinberger A, Dillenbourg P, Harrer A, Hämäläinen R, Häkkinen P & Fischer F
(2007) Specifying computer-supported collaboration scripts. International Journal of
Computer-Supported Collaborative Learning 2(2–3): 211–224.
Kollar I, Fischer F & Hesse FW (2006) Collaboration scripts – A conceptual analysis.
Educational Psychology Review 18: 159–185.
Kreijns K, Kirschner P & Jochems W (2003) Identifying the pitfalls for social interaction
in computer-supported collaborative learning environments: A review of the research.
Computers in Human Behaviour 19: 335–353.
Kreijns K & Kirschner PA (2004) Designing sociable CSCL environments. In: Strijbos
JW, Kirschner PA & Martens RL (eds) What We Know About CSCL: And
Implementing it in Higher Education. Norwell MA, Kluwer Academic Publishers:
221–243.
Krippendorff K (2004) Content analysis: An introduction to its methodology. Thousand
Oaks CA, Sage Publications.
Kruger A (1993) Peer collaboration: Conflict, cooperation or both? Social Development 2:
165–182.
94
Laru J (2012) Scaffolding learning activities with collaborative scripts and mobile devices.
Acta Universitatis Ouluensis. Series E, Scientiae rerum socialium 125. Oulu,
University of Oulu.
Laru J, Näykki P & Järvelä S (2011) Supporting small-group learning using multiple Web
2.0 tools: A case study in the higher education context. The Internet and Higher
Education 15(1): 29–38.
Laru J, Näykki P & Järvelä S (2014) Four stages of research on the educational use of
ubiquitous computing. Learning Technologies, IEEE Transactions 99.
Lave J & Wenger E (1991) Situated Learning. Legitimate peripheral participation.
Cambridge UK, Cambridge University Press.
Lazarus, RS (1991) Emotion and adaptation. Oxford UK, Oxford University Press.
Leinonen P, Järvelä S & Häkkinen P (2005) Conceptualizing awareness of collaboration:
A qualitative study of a global virtual team. Computer Supported Cooperative Work
14(4): 301–322.
Levine JM, Resnick LB & Higgins ET (1993) Social foundations of cognition. Annual
Review of Psychology 44: 585–612.
Light P, Littleton K, Messer D & Joiner R (1994) Social and communicative processes in
computer-based problem solving. European Journal of Psychology of Education 9:
93–109.
Lincoln YS & Guba EG (1985) Naturalistic Inquiry. Thousand Oaks CA, Sage Publication.
Linn MC (2006) The knowledge integration perspective on learning and instruction. In:
Sawyer RK (ed) The Cambridge Handbook of the Learning Sciences. New York NY,
Cambridge University Press: 243–263.
Linnenbrink-Garcia L, Kempler Rogat T & Koskey KLK (2011) Affect and engagement
during small group instruction. Contemporary Educational Psychology 36: 13–24.
Linnenbrink-Garcia L & Pekrun R (2011) Students’ emotions and academic engagement:
Introduction to the special issue. Contemporary Educational Psychology 36: 1–3.
Littleton K & Häkkinen P (1999) Learning together: understanding the processes of
computer-based collaborative learning. In Dillenbourg P (ed) Collaborative Learning:
Cognitive and Computational Approaches. Amsterdam, Pergamon: 20–30.
Littleton K & Miell D (2004) Learning to collaborate, collaborating to learn: editorial
introduction. In: Littleton K, Miell D & Faulkner D (eds) Learning to Collaborate:
Collaborating to Learn. Understanding and promoting educationally productive
collaborative work. New York NY, Nova Science Publishers: 1–6.
Mayring P (2000) Qualitative content analysis. Qualitative Social Research 1(2). URI:
http://www.qualitative-research.net/. Cited 2014/11/17: 120–130.
Mercer N (2008) The seeds of time: why classroom dialogue needs a temporal analysis. Journal
of the Learning Sciences 17(1): 33–59.
Meyer DK & Turner JC (2006) Reconceptualizing emotion and motivation to learn in
classroom contexts. Educational Psychology Review 18: 377–390.
Molenaar I & Järvelä S (2014) Sequential and temporal characteristics of self and socially
shared regulated learning. Metacognition and Learning 9(2): 75–85.
95
Mäkitalo K, Häkkinen P, Järvelä S & Leinonen P (2006) Mechanisms of common ground in
case-based web discussions in teacher education. The Internet and Higher Education 5(3):
247–265.
National Advisory Board on Research Ethics (2012) Good scientific practice and procedures
for handling misconduct and fraud in science. URI: http://www.tenk.fi/sites/tenk.fi/files/
HTK_ohje_2012.pdf. Cited 2014/11/17.
Nelson T, Kruglanski A & Jost J (1998) Knowing Thyself and Others: Progress in
Metacognitive Social Psychology. In: Yzerbyt V, Lories G & Dardenne B (eds)
Metacognition. Cognitive and Social Dimensions. London UK, Sage Publications: 69–89.
Neuendorf KA (2002) The Content Analysis Guidebook. Thousand Oaks CA, Sage
Publications.
Näykki P & Järvelä S (2008) How pictorial knowledge representations mediate collaborative
knowledge construction in groups. Journal of Research on Technology in Education 40(3):
359–388.
Näykki P, Järvelä S, Kirschner P & Järvenoja H (2014) Socio-emotional conflict in
collaborative learning – A process-oriented case study in a higher education context.
International Journal of Educational Research 68: 1–14.
Näykki P, Järvenoja H, Järvelä S & Kirschner P (2014) Monitoring as a regulation activity in
higher education students’ collaborative learning – Quality and temporal variation.
Manuscript submitted for publication.
O’Donnell A (2006) The role of peers and group learning. In: Alexander P & Winne P (eds)
Handbook of Educational Psychology (2nd ed.). Mahwah NJ, Lawrence Erlbaum
Associates: 781–802.
O’Donnell AM & King A (eds) (1999) Cognitive Perspectives on Peer Learning. Mahwah NJ,
Lawrence Erlbaum.
O’Donnell AM & O’Kelly J (1994) Learning from peers: Beyond the rhetoric of positive
results. Educational Psychology Review 6(4): 321–349.
OECD (2013). Draft PISA 2015 collaborative problem solving framework. URI:
http://www.oecd.org/pisa/pisaproducts/Draft%20PISA%202015%20Collaborative%20Pro
blem%20Solving%20Framework%20.pdf. Cited 2014/11/17.
Op’t Eynde P, De Corte E & Verschaffel L (2007) Students’ emotions: A key component
of self-regulated learning? In: Schutz PA & Pekrun R (eds) Emotion in Education.
Burlington MA, Elsevier: 185–204.
Paavola S, Lipponen L & Hakkarainen K (2004) Models of Innovative Knowledge
Communities and Three Metaphors of Learning. Review of Educational Research
74(4): 557–576.
Palincsar A & Brown A (1984) Reciprocal teaching of comprehension-fostering and
comprehension-monitoring activities. Cognition and Instruction 1: 117–175.
Patton, MQ (2002) Qualitative Evaluation and Research Methods. Thousand Oaks CA,
Sage Publications.
Pea R (1993) Practices of distributed intelligence and designs for education. In: Salomon G
(ed) Distributed Cognitions: Psychological and Educational Considerations.
Cambridge UK, University Press: 47–87.
96
Pekrun R, Goetz T, Titz W & Perry RP (2002) Academic emotions in students’ selfregulated learning and achievement: A program of quantitative and qualitative
research. Educational Psychologist 37: 91–106.
Piaget J (1985) The Equilibration of Cognitive Structures. The Central Problem of
Intellectual Development. Chicago IL, University of Chicago Press.
Pintrich PR (2000) Role of goal orientation in self-regulated learning. In: Boekarts M,
Pintrich P & Zeidner M (eds) Handbook of Self-Regulation. San Diego CA,
Academic Press: 452–494.
Pintrich PR (2002) The role of metacognitive knowledge in learning, teaching and
assessing. Theory into Practice 41(4): 219–225.
Pintrich PR & de Groot EV (1990) Motivational and self-regulated learning components of
classroom academic performance. Journal of Educational Psychology 82(1): 33–40.
Pintrich PR, Wolters C & Baxter G (2000) Assessing metacognition and selfregulated learning. In: Schraw G & Impara J (eds) Issues in the Measurements of
Metacognition. Lincoln, NE, Buros Institute of Mental Measurements: 43–98.
Ploetzner R, Dillenbourg P, Praier M & Traum D (1999) Learning by explaining to oneself
and to others. In: Dillenbourg P (ed) Collaborative-Learning: Cognitive and
Computational Approaches. Oxford UK, Elsevier: 103–121.
Puntambekar S (2013) Mixed methods for analyzing collaborative learning. In: HmeloSilver CE, Chinn CA & O’Donnell AM (eds) The International Handbook of
Collaborative Learning. New York NY, Routledge: 187–195.
Rafal C (1996) From co-construction to takeovers: Science talk in a group of four girls.
Journal of the Learning Sciences 5(3): 279–293.
Reimann P (2009) Time is precious: Variable and event-centered approaches to process
analysis in CSCL research. International Journal of Computer-Supported Collaborative
Learning 4(3): 239–257.
Reimann P, Yasef K & Kay J (2011) Analyzing collaborative interactions with data mining
methods for the benefit of learning. In: Puntambekar S, Erkens G & Hmelo-Silver C (eds)
Analyzing Interactions in CSCL – Methods, Approaches and Issues. New York NY,
Springer: 161–186.
Resnick, L (1991) Shared cognition: thinking as social practice. In: Resnick L, Levine J &
Teasley S (eds), Perspectives on Socially Shared Cognition. Washington DC,
American Psychological Association: 1–23.
Rimé B (2007) Interpersonal emotion regulation. In; Gross JJ (ed) Handbook of Emotion
Regulation. New York NY, Guilford: 466–487.
Rogat TK & Linnenbrink-Garcia L (2011) Socially shared regulation in collaborative
groups: An analysis of the interplay between quality of social regulation and group
processes. Cognition & Instruction 29(4): 375–415.
Roschelle J (1992) Learning by collaborating: Convergent conceptual change. The Journal of
the Learning Sciences 2(3): 235–276.
Roschelle J & Teasley S (1995) The construction of shared knowledge in collaborative problem
solving. In: O’Malley CE (ed) Computer Supported Collaborative Learning. Heidelberg,
Springer-Verlag: 69–97.
97
Roscoe RD & Chi MTH (2008) Tutor learning: the role of explaining and responding to
questions. Instructional Science 36(4): 321–350.
Rotherham AJ & Willingham D (2009) 21st century skills: The challenges ahead. Educational
Leadership, 67(1): 16–21.
Saab N (2012) Team regulation, regulation of social activities or co-regulation: Different
labels for effective regulation of learning in CSCL. Metacognition and Learning 7(1):
1–6.
Saab N, van Joolingen W & van Hout-Wolters B (2012) Support of the collaborative inquiry
learning process: influence of support on task and team regulation. Metacognition and
Learning 7: 7–23.
Salomon G (1993) No distribution without individual’s cognition: a dynamic interactional
view. In: Salomon G (ed) Distributed Cognitions. Psychological and Educational
Considerations. Cambridge UK, Cambridge University Press: 111–138.
Salomon G & Globerson T (1989) When teams do not function the way they ought to.
International Journal of Educational Research 13(1): 89–100.
Salomon G & Perkins D (1998) Individual and social aspects of learning. Review of
Research in Education 23: 1–24.
Salonen P, Vauras M & Efklides A (2005) Social interaction – What can it tell us about
metacognition and coregulation of learning? European Psychologist 10: 199–208.
Sangin M, Molinari G, Nüssli M & Dillenbourg P (2011) Facilitating peer knowledge
modeling: Effects of a knowledge awareness tool on collaborative learning outcomes
and processes. Computers in Human Behavior 27: 1059–1067.Sargent LD & SueChan C (2001) Does Diversity Affect Group Efficacy? The Intervening Role of
Cohesion and Task Interdependence. Small Group Research 32(4): 426–450.
Savery JR (2006) Overview of problem-based learning. Definitions and distinctions. The
Interdisciplinary Journal of Problem-Based Learning 1: 9–20.
Sawyer RK (2003) Group Creativity: Music, Theater, Collaboration. Mahwah NJ, Erlbaum.
Sawyer RK (2006) The new science of learning. In: Sawyer K (ed) The Cambridge
Handbook of Learning Sciences. New York NY, Cambridge University Press: 1–16.
Sawyer RK (2013) Qualitative Methodologies for Studying Small Groups. In: HmeloSilver C, Chinn C, Chan C & O’Donnell A (eds) International Handbook of
Collaborative Learning. New York NY, Routledge: 126–148.
Scardamalia M & Bereiter C (2003) Knowledge building. In: Guthrie JW (ed)
Encyclopedia of Education (2nd ed.). New York NY, Macmillan: 1370–1373.
Scardamalia M & Bereiter C (2006) Knowledge building: Theory, pedagogy, and
technology. In: Sawyer K (ed) Cambridge Handbook of the Learning Sciences. New
York NY, Cambridge University Press: 97–118.
Scherer KR & Tran V (2001) Effects of emotion on the process of Organization Learning.
In: Nonaka I (ed) Handbook of Organizational Learning and Knowledge. Oxford UK,
Oxford University Press: 369–392.
Schraw G (1998) Promoting general metacognitive awareness. Instructional Science 26(1–
2): 113–125.
98
Schunk DH & Zimmerman BJ (eds) (1998) Self-Regulated Learning: From Teaching to
Self-Reflective Practice. New York NY, Guilford Press.
Schunk DH & Zimmerman BJ (eds) (2008) Motivation and Self-Regulated Learning:
Theory, Research, and Applications. New York NY, Lawrence Erlbaum Associates.
Schutz PA & Davis HA (2000) Emotions and self-regulation during test taking.
Educational Psychologist 35: 243–256.
Schutz PA & Pekrun R (eds) (2007) Emotion in Education. San Diego CA, Academic
Press.
Schwartz DL (1995) The emergence of abstract representations in dyad problem solving.
Journal of the Learning Sciences 4: 321–354.
Schwartz DL, Chang J & Martin L (2008) Instrumentation and innovation in design
experiments: Taking the turn towards efficiency. In: Kelly AE, Lesh RA & Baek JY
(eds) Handbook of design research methods in education: Innovations in science,
technology, engineering, and mathematics learning and teaching. New York,
Routledge: 47–65.
Sfard A (1998) On two metaphors for learning and the dangers of choosing just one.
Educational Researcher 27(2): 4–13.
Shirouzu H, Miyake N & Masukawa H (2002) Cognitively active externalization for situated
reflection. Cognitive Science 26(4): 469–501.
Silverman D (2001) Interpreting Qualitative Data: Methods for Analyzing Talk, Text and
Interaction. London UK, Sage Publications.
Sommet N, Darnon C, Mugny G, Quiamzade A, Pulfrey C, Dompnier B & Butera F (2012)
Performance goals in conflictual social interactions: Toward the distinction between two
modes of relational conflict regulation. British Journal of Social Psychology 53(1): 134–
153.
Son LK & Schwartz BL (2002) The relation between metacognitive monitoring and control. In:
Perfect TJ & Schwatz BL (eds), Applied metacognition. Cambridge UK, Cambridge
University Press.
Stahl G (2006) Group Cognition: Computer Support for Collaborative Knowledge Building.
Cambridge MA, MIT Press.
Stahl G, Koschmann T & Suthers D (2006) Computer-Supported Collaborative Learning. In:
Sawyer RK (ed) The Cambridge Handbook of the Learning Sciences. New York NY,
Cambridge University Press: 409–425.
Stake RE (2003) Case Studies. In: Denzin NK & Lincoln Y (eds) Strategies of Qualitative
Inquiry. London UK, Sage Publications: 134–164.
Stenbacka C (2001) Qualitative research requires quality concepts of its own. Management
Decision 39(7): 551–555.
Strijbos J & Fischer F (2007) Methodological challenges for collaborative learning research.
Learning and Instruction 17: 389–393.
Strijbos JW, Martens RL, Prins FJ & Jochems WMG (2006) Content analysis: What are they
talking about? Computers & Education 46: 29–48.
99
Summers M & Volet SE (2010) Group work does not necessarily equal collaborative learning:
Evidence from observations and self-reports. European Journal of Psychology of
Education 25(4): 473–492.
Teasley S (1997) Talking about reasoning: How important is the peer in peer collaboration? In:
Resnick R, Säljö R, Pontecorvo C & Burge B (eds), Discourse, Tools and Reasoning:
Essays on Situated Cognition. Berlin, Springer: 361–384.
Tesch R (1990) Qualitative Research Analysis Types and Software Tools. New York NY,
Falmer.
Thompson L & Fine G (1999) Socially shared cognition, affect, and behavior: A review
and integration. Personality and Social Psychology Review 3(4): 278–302.
Thompson RA, Laible DJ & Ontai LL (2003) Early understanding of emotion, morality,
and the self: Developing a working model. In: Kail RV (ed) Advances in Child
Development and Behavior. San Diego CA, Academic Press: 137–171.
Tuckman B (1965) Developmental sequence of small groups. Psychological Bulletin 63(6):
384–399.
Turner JC (2006) Measuring Self-Regulation: A Focus on Activity. Educational
Psychology Review 18: 293–296.
Van Boxtel C, Van der Linden J & Kanselaar G (2000) Collaborative learning tasks and
the elaboration on conceptual knowledge. Learning and Instruction 10: 311–330.
Van den Bossche P, Gijselaers W, Segers M & Kirschner PA (2006) Social and cognitive
factors driving teamwork in collaborative learning environments. Small Group
Research 37(5): 490–521.
Van den Bossche P, Gijselaers W, Segers M, Woltjer G & Kirschner P (2011) Team
learning: building shared mental models. Instructional Science 39(3): 283–301.
Van der Linden J, Erkens G, Schmidt H & Renshaw P (2000) Collaborative learning. In
Simons RJ, Linden JVD and Duffy T (eds) New Learning. London, Kluwer Academic
Publishers: 37–57.
Van Gog T, Paas F, Van Merriënboer JJG & Witte P (2005) Uncovering the problemsolving process: Cued retrospective reporting versus concurrent and retrospective
reporting. Journal of Experimental Psychology: Applied 11: 237–244.
Vauras M, Iiskala T, Kajamies A, Kinnunen R & Lehtinen E (2003) Shared regulation and
motivation of collaborating peers: A case analysis. Psychologia, an International Journal
of Psychology in the Orient 46: 19–37.
Veenman S, Denessen E, van den Akker A & van der Rijt J (2005) Effects of a cooperative
learning program on the elaborations of students during help seeking and help giving.
American Educational Research Journal 42(1): 115–151.
Volet SE & Mansfield C (2006) Group work at university: Significance of personal goals in the
regulation strategies of students with positive and negative appraisals. Higher Education,
Research and Development 25: 341–356.
Volet S, Summers M & Thurman J (2009) High-level co-regulation in collaborative learning:
How does it emerge and how is it sustained? Learning and Instruction 19(2): 128–143.
Volet S & Vauras M (2013) Interpersonal Regulation of Learning and Motivation:
Methodological Advances. New York NY, Routledge.
100
Volet S, Vauras M & Salonen P (2009) Self- and social regulation in learning contexts: an
integrative perspective. Educational Psychologist 44(4): 215–226.
von Glasersfeld E (1982) An interpretation of Piaget’s constructivism. Revue Internationale de
Philosophie 36(4): 612–35.
Vygotsky L (1978) Mind in Society: The Development of Higher Psychological Processes.
Cambridge MA, Harvard University Press.
Vygotsky LS (1997) Volume 3: Problems of the Theory and History of Psychology. In: Reiber
R & Wollock J (eds) The Collected Works of L. S. Vygotsky. Translated by R. van der
Veer. London UK, Plenum Press.
Webb NM (1989) Peer interaction and learning in small groups. International Journal of
Educational Research 13: 21–39.
Webb NM (1991) Task-related verbal interaction and mathematics learning in small groups.
Journal of Research in Mathematics Education 22(5): 366–389.
Webb NM, Troper JD & Fall R (1995) Constructive activity and learning in collaborative small
groups. Journal of Educational Psychology 87(3): 406–423.
Weber RP (1990) Basic Content Analysis (2nd ed.). Series: Quantitative Applications in the
Social Sciences. Newbury Park CA, SAGE Publications.
Weinberger A, Ertl B, Fischer F & Mandl H (2005) Epistemic and social scripts in computersupported collaborative learning. Instructional Science 33: 1–30.
Weinberger A & Fischer F (2006) A framework to analyze argumentative knowledge
construction in computer-supported collaborative learning. Computers & Education 46:
71–95.
Weinberger A, Stegmann K & Fischer F (2007) Knowledge convergence in collaborative
learning: Concepts and assessment. Learning and Instruction 17: 416–426.
Whitebread D, Bingham S, Grau V, Pino Pasternak D & Sangster C (2007) Development of
metacognition and self-regulated learning in young children: role of collaborative and
peer-assisted learning. Journal of Cognitive Education and Psychology 6: 433–455.
Whitebread D, Coltman P, Pino Pasternak D, Sangster C, Grau V, Bingham S, Almeqdad
Q & Demetriou D (2009) The development of two observational tools for assessing
metacognition and self-regulated learning in young children. Metacognition and
learning 4: 63–85.
Wilhelm J, Sherrod S & Walters K (2008) Project-based learning environments: Challenging
pre-service teachers to act in the moment. Journal of Educational Research 101: 220-233.
Williams EA, Duray R & Venkateshwar R (2006) Teamwork orientation, group cohesiveness,
and student learning: A study of the use of teams in online distance education. Journal of
Management Education 30(4): 592–616.
Winne PH (1997) Experimenting to bootstrap self-regulated learning. Journal of Educational
Psychology 89: 397–410.
Winne PH (2011) Cognitive and metacognitive factors in self-regulated learning. In:
Zimmerman BJ & Schunk DH (eds) Handbook of Self-Regulation of Learning and
Performance. New York NY, Routledge: 15–32.
101
Winne PH & Hadwin AF (1998) Studying as self-regulated learning. In: Hacker DJ, Dunlosky
J & Graesser AC (eds) Metacognition in Educational Theory and Practice. Mahwah NJ,
Lawrence Erlbaum Associates: 277–304.
Winne PH, Hadwin AF & Perry NE (2013) Metacognition and computer-supported
collaborative learning. In: Hmelo-Silver C, O’Donnell A, Chan C & Chinn C (eds)
International Handbook of Collaborative Learning. New York NY, Taylor and Francis:
462–479.
Winne PH & Perry NE (2000) Measuring self-regulated learning. In: Boekaerts M, Pintrich P
& Zeidner M (eds) Handbook of Self-Regulation. San Diego CA, Academic Press: 532–
566.
Wittrock MC (1990) Generative processes of comprehension. Educational Psychologist 24(4):
345–376.
Wolters CA (2003) Regulation of motivation: Evaluating an underemphasized aspect of selfregulated learning. Educational Psychologist 38: 189–205.
Yin R (2009) Case Study Research: Design and Methods. Thousand Oaks CA, Sage
Publications.
Zimmerman BJ (1989) A social cognitive view of self-regulated academic learning. Journal of
Educational Psychology 81(3): 329–339.
Zimmerman BJ (2000) Attaining self-regulation. A social cognitive perspective. In: Boekaerts
M, Pintrich PR & Zeidner M (eds), Handbook of Self-Regulation. San Diego CA,
Academic Press: 13–39.
Zimmerman BJ (2002) Becoming a self-regulated learner: An overview. Theory into Practice
41(2): 64–70.
Zimmerman BJ (2008) Investigating self-regulation and motivation: Historical background,
methodological developments, and future prospects. American Educational Research
Journal 45(1): 166–183.
102
Original articles
I
Näykki P & Järvelä S (2008) How pictorial knowledge representations mediate
collaborative knowledge construction in groups. Journal of Research on Technology
in Education 40(3): 359–388.
II Näykki P, Järvenoja H, Järvelä S & Kirschner P (2014) Monitoring as a regulation
activity in higher education students’ collaborative learning – Quality and temporal
variation. Manuscript submitted for publication.
III Näykki P, Järvelä S, Kirscher P & Järvenoja H (2014) Socio-emotional conflict in
collaborative learning – A process-oriented case study in a higher education context.
International Journal of Educational Research 68: 1–14.
IV Järvelä S, Järvenoja H & Näykki P (2014) Analyzing regulation of motivation as an
individual and social process – a situated approach. In: Volet S & Vauras M (eds)
Interpersonal regulation of learning and motivation: Methodological advances.
Abingdon UK, Routledge: 170–187.
Reprinted with permission from Taylor & Francis publications (Article I),
Elsevier (Article III) and Taylor & Francis Books (Article IV). Original
publications are not included in the electronic version of the dissertation.
103
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135.
Belt, Aino (2013) Kun työrauha horjuu : kotitalousopettajien käsityksiä
työrauhahäiriöistä ja niiden taustatekijöistä
136.
Autti, Outi (2013) Valtavirta muutoksessa : vesivoima ja paikalliset asukkaat
Kemijoella
137.
Pääkkönen, Leena (2013) Nuorten musisointiprosessi koulussa toteutetussa
konserttiprojektissa : musiikkiluokkalaisten kertomukset yhdessä tekemisestä
138.
Kontio, Mari (2013) Jaetun ymmärryksen rakentuminen moniammatillisten
oppilashuoltoryhmien kokouksissa
139.
Oinas-Kukkonen, Heikki (2013) Alkoholistin ja hänen läheisensä samanaikainen
toipuminen vapauttavana oppimisprosessina Minnesota-hoidossa
140.
Alanko, Anu (2013) Osallisuusryhmä demokratiakasvatuksen tilana
tapaustutkimus lasten ja nuorten osallisuusryhmien toiminnasta Oulussa
141.
Puroila, Anna-Maija & Kaunisto, Saara-Leena & Syrjälä, Leena(Toim.) (2013)
Naisen äänellä : professori Eila Estolan juhlakirja
142.
Malmberg, Jonna (2014) Tracing the process of self-regulated learning – students’
strategic activity in g/nStudy learning environment
143.
Virkkula, Esa (2014) ”Soittaminen ammattilaisen kanssa on paras tapa oppia” :
työpajaperustainen työssäoppiminen muusikoiden ammatillisessa koulutuksessa
144.
Lutovac, Sonja (2014) From memories of the past to anticipations of the future :
pre-service elementary teachers’ mathematical identity work
145.
Villanen, Heli (2014) Our place, my future and their project : reflecting children’s
lifeworld in education for sustainable development
146.
Kaunisto, Saara-Leena (2014) ”Täällä sai puhua sen, mitä kenties muualla ei” :
opettajien vertaisryhmä kerronnallisena ympäristönä
147.
Suorsa, Teemu (2014) Todellisinta on mahdollinen : systeeminen ja
subjektitieteellinen näkökulma kasvatuspsykologiseen kokemuksen tutkimukseen
148.
Salo, Raimo (2014) Opettajien osaamisen
perusopetukseen valmistavassa opetuksessa
149.
Hanhela, Teemu (2014) Educational perspectives on recognition theory
150.
Rantanen, Antti (2014) Development of methodology for assessing counseling
interactions : developing the Counselor Response Observation System and
assessing applicability of heart rate variability to the measurement of client
emotions during verbal reporting
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