Mutual Modeling Workshop
Villars, Switzerland
22-23 January 2007
Workshop Organizers
(CRAFT, School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne – EPFL, Switzerland)
1. Gaëlle Molinari ([email protected]), Postdoc Researcher
2. Mirweis Sangin ([email protected]), PhD Student
3. Nicolas Nova ([email protected]), PhD Student
About the “Mutual models in collaborative tasks” workshop
When working and learning together, group members construct and maintain
some representation of their partners' knowledge, beliefs and goals by a process
we refer to as “mutual-modeling”. This process is hypothesized to play a crucial
role in the emergence of a shared understanding among group members. More
practically, knowing what the partner has understood and what (s)he might do
next may help a team of interdependent workers solve problems more efficiently.
An alternative hypothesis is that learners do not build an individualized model of
each of their partners, but rather a global model of the group (or both). In each
case, since this modeling activity lead collaborative partners to think more deeply
about the task, does it contribute to improve their knowledge of the task at hand?
If it does, can we design collaborative learning applications that scaffold the mutual modeling process?
Similar questions have been addressed under a variety of concepts: i.e., intersubjectivity, audience design, transactive memory, perspective taking, team mental model, etc. The first objective of this workshop is to explore the core idea underlying these concepts but also the specificity of each approach. Contributions
from a broad range of academic fields will raise these differences and hopefully
bring forward key aspects of the mutual modeling process. The second goal of
this workshop is to address the methods and tools for investigating mutual models. Indeed assessing modeling activity poses methodological problems: realtime assessment biases the spontaneous mutual modeling mechanisms while a
posteriori assessment raises well-known memory and rationalization issues. By
comparing empirical studies, we will discover various ways - including eye tracking methods- to estimate something that can hardly be measured.
This workshop stems from a Swiss National Science Foundation project entitled
"The Effects of Mutual Modeling on Collaborative Learning". This project is carried out at CRAFT (Center for Research and Support of Training and its Technologies) in Swiss Federal Institute of Technology in Lausanne by Gaëlle Molinari,
Mirweis Sangin, Nicolas Nova, under the direction of Prof. Pierre Dillenbourg.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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List of participants
1. Michael Baker ([email protected]), CNRS Researcher, UMR MODYCO,
Université Paris 10, France.
2. Adrian Bangerter ([email protected]), Professor, Institut de Psychologie du Travail et des Organisations, Université de Neuchâtel, Switzerland.
3. Sophie Bettex ([email protected]), Research Assistant, Institut de Psychologie du Travail et des Organisations, Université de Neuchâtel, Switzerland.
4. Céline Buchs ([email protected]), Assistant Professor, Faculté de
Psychologie et des Sciences de l’Education, Université de Genève, Switzerland.
5. Fabrizio Butera ([email protected]), Professor, Institut des Sciences Sociales et Pédagogiques (ISSP), Université de Lausanne, Switzerland.
6. Béatrice Cahour ([email protected]), CNRS Researcher, UMR IRIT/Equipe GRIC,
Université Paul Sabatier, France.
7. Justine Cassell ([email protected]), Director of the new Center for
Technology & Social Behavior, Professor, Media, Technology & Society, Northwestern University, USA.
8. Eric Chevalley ([email protected]), PhD Student, Institut de Psychologie
du Travail et des Organisations, Université de Neuchâtel, Switzerland.
9. Jessica Dehler ([email protected]), PhD Student, University of Tuebingen,
Germany.
10. Tanja Engelmann ([email protected]), Postdoc Researcher,
Knowledge Media Research Center, University of Tuebingen, Germany.
11. Darren Gergle ([email protected]), Assistant Professor, Media, Technology & Society, Northwestern University, USA.
12. William S. Horton (whorton@ northwestern.edu), Assistant Professor, Department of Psychology, Northwestern University, USA.
13. Kristine Lund ([email protected]), Research Ingenior, UMR ICAR, Université Lyon 2, France.
14. Richard Moreland ([email protected]), Professor, Department of Psychology,
University of Pittsburgh, USA.
15. Christian Roßnagel ([email protected]), Professor, Jacobs Center for
Lifelong Learning and Institutional Development, International University Bremen,
Germany.
16. Daniel D. Suthers ([email protected]), Assistant Professor, Department of Information and Computer Sciences, University of Hawai'i, Manoa.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Workshop Program
Sunday 21 January, 2007
19:00 M. Sangin & N.
Workshop introduction
20:00 Nova
Monday 22 January, 2007
8:30
8.50
M. Sangin & N.
Introduction
Nova
8:50
9:30
D. D. Suthers
9:30
10:00
M. Sangin
10:00
10:30
T. Engelmann
10:30
10:50
Coffee Break
10:50
11:20
J. Dehler
11:20
12:00
F. Butera
12:00
12:40
C. Buchs
12:40
16:30
Lunch + Ski Break
16:30
17:10
M. Baker
17:10
17:40
17:40
18:20
Technology Affordances for Intersubjective Meaningmaking: A Research Agenda for CSCL
“I See What You Know”: Do Awareness Tools Enhance
Mutual Modeling During Collaborative Learning?
Knowledge and Information Awareness for Fostering
Computer-Supported Collaborative Problem Solving of
Spatially Distributed Group Members
Fostering Audience Design and Learning of ComputerMediated Knowledge Communication with Knowledge
Mirroring
Conflicts and Achievement Goals in Learning with a Partner
The Role of Information Distribution during Cooperative
Dyadic Work regarding the Perception of Partner and
Learning: Partner as a Threat for Competence or a Useful
Help
Mutuality as the cream on the milk
Analysis of Participants’ Computer-Mediated Distance
K. Lund, C. Ros- Cooperation compared to their Own View of their Coopsetti, & S. Metz
eration
? (How do body behaviors contribute to collaborative diaJ. Cassell
logue?)
18:20
19:00
D. Gergle
?19:00
19:20
Coffee Break
19:20
20:20
General discussion
Shared Visual Information as a Window into Other Minds
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Tuesday 23 January, 2007
Memory-Based Contributions to Conversational
Common Ground
Cognitive Load and Audience Design: Interference
with Construction and Updating of an Adequate
Partner Model
8:30
9:10
W. S. Horton
9:10
9:50
C. Roßnagel
9:50
10:30
R. Moreland
10:30
10:50
Coffee Break
10:50
11:30
N. Nova
Evaluating Mutual Modeling in CSCW Environments
11:30
12:10
B. Cahour
Investigating the Subjective and Situated Experience
of Mutual Modelling
12:10
16:30
Lunch + Ski Break
16:30
17:00
E. Chevalley & Suspending and Reinstating Collaborative Tasks:
A. Bangerter
Facework and Grounding
17:00
Transactive Memory and Work Group Performance
General Discussion
18:00
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Technology Affordances for Intersubjective Meaning-making: A
Research Agenda for CSCL
Daniel D. Suthers
Department of Information and Computer Sciences, University of Hawaii, Manoa
[email protected]
Now well into its second decade, the field of Computer Supported Collaborative Learning
(CSCL) appears healthy, while encompassing a diversity of topics of study, methodologies, and representatives of various research communities. It is an appropriate time to
ask: what central questions can integrate our work into a coherent field? This paper proposes the study of technology affordances for intersubjective meaning-making as an integrating research agenda for CSCL. A brief survey of epistemologies of collaborative
learning and forms of computer support for that learning characterize the field to be integrated and motivate the proposal. A hybrid of experimental, descriptive and design
methodologies is proposed in support of this agenda. A working definition of intersubjective meaning-making as joint composition of interpretations of a dynamically evolving
context is provided, and used to propose a framework around which dialogue between
analytic approaches can take place.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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“I See What You Know”: Do Awareness Tools Enhance Mutual
Modeling During Collaborative Learning?
Mirweis Sangin, Gaëlle Molinari, Pierre Dillenbourg
CRAFT, School of Computer Sciences and Communication, Ecole Polytechnique
Fédérale de Lausanne (EPFL), Switzerland
{mirweis.sangin, gaelle.molinari, pierre.dillenbourg} @epfl.ch
In the CSCL field, collaborative learning is conceptualized as an activity in which shared
knowledge is constructed by partners through their interactions with each other and with
the environment. These activities require that the co-learners build some representation
of their partners’ knowledge, beliefs and goals. This process of building assumptions
about the beliefs and the knowledge of their partner(s) is called mutual modeling. Mutual
modeling in everyday life involves a large variety of mental states such as knowledge,
behavior, beliefs, desires, intentions, emotions, traits, attitudes. The study reported here
focuses on an important aspect of mutual modeling: inferences about peers’ knowledge.
Do awareness cues about the partner’s knowledge enhance learning performance of
pairs? We report an empirical study that addresses this question. In a first step, learners
have to read and assimilate individually a text about concepts (i.e. neural transmission)
split into 3 different ‘chapters’. They are then invited to answer a multiple choice questionnaire to measure their comprehension. In a second step, learners have to collaborate
at a distance concept-map building task. During this phase, learners of the experimental
condition are provided with estimation of their partner’s knowledge about the 3 chapters
(computed thanks to the learners’ answers to the questionnaire). In the control condition
learners do not have this ‘awareness tool’ of their peer’s knowledge. Finally, learners’
understanding is tested again after the collaborative task through the same multiple
choice questionnaire to measure the collaborative learning gain. This experiment addresses two main questions: does a ‘knowledge awareness tool’ influence the quality of
interactions by means of processes such as audience design (Clark & Murphy, 1982)
and perspective taking and making (Boland & Tenkasi, 1995); by facilitating the mutual
modeling processes, do we enhance collaborative learning performance?
This talk will present preliminary quantitative results and describe some of the innovative methodologies and technologies used to assess social and cognitive processes
involved in mutual modeling. Indeed, in order to avoid the ‘anticipation’ and ‘rationalization’ biases that self-reported questionnaire may trigger, we use interaction analyses and
automatic parallel gaze analysis. We therefore acquired two eye-tracking machines and
computed automatic comparison of the eye paths of both learners during the collaborative task. These techniques are expected to provide promising possibilities for finegrained analysis.
References
Boland, R. J., & Tenkasi, R. V. (1995). Perspective making and perspective taking in
communities of knowing. Organization Science, 6(4), 350-372.
Clark, H. H., & Murphy, G. L. (1982). Audience design in meaning and reference. In J.-F.
Le Ny & W. Kintsch (Eds.), Language and comprehension. New York: North-Holland.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Knowledge and Information Awareness for Fostering ComputerSupported Collaborative Problem Solving of Spatially Distributed Group Members
Tanja Engelmann
Knowledge Media Research Center, University of Tuebingen, Germany
[email protected]
In today’s information society, computer-supported collaborative learning (CSCL) has
become increasingly important. However, there are still problems regarding CSCL, especially interaction problems within groups, like problems in communication and cooperation. One possibility to support CSCL is to evoke knowledge and information awareness, that is, awareness of group members with regard to the knowledge and the information underlying this knowledge of their collaborators (see Keller, Tergan & Coffey,
2006). In the talk, a study will be presented that investigated whether knowledge and
information awareness is an efficient means to foster computer-supported collaborative
problem solving.
In this study, an experimental condition, in which the group members had
“knowledge and information awareness” was compared to a control condition, in which
the group members did not have it. The knowledge and information awareness in the
experimental condition was evoked by possibility of visualizations that allow a group
member to become aware of the knowledge and information of the collaborators of a
group.
Participants were 90 students of different fields of study of the University of
Tuebingen (Germany). They worked in groups of three participants, each participant sitting in a separate room section. They could not see, but speak with each other. The participants of a group had to take over the roles of experts who had to care for a fictitious
kind of spruce forest by solving several problems. The information units were evenly distributed among the three experts. Each participant had access to several unshared, with
one expert shared or with both experts shared information units. After an individual
phase in which they had to access their information units and to structure their
knowledge and information by means of knowledge and information visualization by using CmapTools (a digital Concept Mapping Tool developed by the Florida Institute for
Human and Machine Cognition, USA), they started to collaborate. In this collaborative
phase they could speak with each other. Firstly, they had to compile their knowledge and
information as well as to create a common knowledge and information visualization.
Secondly, they had to solve the problem solving tasks by using their common knowledge
and information visualization. In the control condition they had access to their own
knowledge and information visualization only as well as to their shared working window
for creating the common visualization. In the experimental condition, the participants had
also access to the knowledge and information visualizations of their collaborators.
Results showed that the knowledge and information visualizations of the collaborators were used and evaluated as useful. In addition, the knowledge and information
visualizations of the collaborators evoked knowledge and information awareness. Further analyses showed that knowledge and information awareness reduced cognitive
load, resulted in clearer group maps, and fostered problem solving.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Further studies will focus on the mechanisms that caused the positive effect of
knowledge and information awareness on computer-supported collaborative problem
solving of spatially distributed group members.
References
Keller, T., Tergan, S. -O., & Coffey, J. (2006). Concept maps used as a “knowledge and
information awareness” tool for supporting collaborative problem solving in distributed groups. In A. J. Cañas, & J. D. Novak (Eds.), Concept Maps: Theories, Methodology, Technology. Proceedings of the Second International Conference on Concept
Mapping (pp. 128-135).
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Fostering Audience Design and Learning of Computer-Mediated
Knowledge Communication with Knowledge Mirroring
Jessica Dehler
Knowledge Media Research Center, University of Tuebingen, Germany
[email protected]
Recently, the field of CSCL evinces interest in adopting the notion of awareness from
CSCW literature. Analysis of collaborative tasks helps to deduce the type of information
being useful for CSCL awareness tools. Generally, given the task of knowledge acquisition and exchange learners will find information about their collaborators´ knowledge
helpful. More specifically, for peer-tutoring (as one typical collaborative activity) to be effective learners need to construct adequate models of their partner’s knowledge. However, initial models are biased towards the model of one’s own knowledge and common
strategies of verifying models are frequently ineffective. Thus, within computer-mediated
peer-tutoring learners face problems with constructing mutual models, establishing
common ground, and adapting communication to the specific partner.
`Knowledge Mirroring` (KM), realized as providing information about the partner’s
knowledge, is introduced as technological support developed to compensate for these
problems. The impact of KM on communication and learning is studied in a simulated
peer-tutoring scenario with explaining as basic activity.
42 participants were randomly assigned to two conditions (with versus without
KM). Subjective estimations of understanding were assessed during an individual learning phase. During formulation of explanations in the control condition only subjects´ own
knowledge was presented in the KM-Tool. Subjects in the experimental condition were
additionally provided with the knowledge of a simulated partner. The partner’s
knowledge was computed systematically relative to the participants´ knowledge resulting
in three types of combinations: shared knowledge, shared deficit, and complementary
knowledge of participant.
Analysis of explanations revealed audience design with respect to usage of elaborations and references. That is, explanations of complementary knowledge in the experimental condition contained higher ratios of elaborations and lower ratios of references compared to the CC-baseline explanations of participant knowledge. Results regarding knowledge acquisition indicated that learners provided with KM performed better
on subtests of inferential knowledge.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Conflicts and Achievement Goals in Learning with a Partner
Fabrizio Butera
Institut des Sciences Sociales et Pédagogiques (ISSP), University of Lausanne, Switzerland
[email protected]
Little work has studied achievement goals in social interaction situations. The present
work aimed at contributing to this matter by showing the potential of social interaction (in
particular disagreement) to moderate the effects of achievement goals on learning and
conflict regulation. In the first experiment, participants were led to think they interacted
with a partner, sharing opinions about a text that they were studying. Mastery and performance goals were manipulated. During the “interaction”, they received either disagreement or agreement from this bogus partner. Results showed that a condition in
which mastery goals were induced led to better learning than a performance goal condition only when the partner disagreed. No differences between goal conditions were observed when the partner agreed. In the second study, participants interacted about conflictual issues in a context enhancing performance goals, mastery goals or no goal. The
amount of disagreement within the interaction was measured. Results indicated that disagreement predicted epistemic conflict regulation (focused on task comprehension) in
the mastery-goals condition, while it predicted relational conflict regulation (focused on
affirmation of competence) in the performance-goals condition. Implications for achievement goals and for socio-cognitive conflict research are discussed, stressing the importance of the representation of the partner in social interaction.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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The Role of Information Distribution during Cooperative Dyadic
Work regarding the Perception of Partner and Learning: Partner
as a Threat for Competence or a Useful Help
Céline Buchs
Faculté de Psychologie et des Sciences de l’Education, University of Geneva, Switzerland
[email protected]
Our main objective is to investigate peer learning approaches in order to improve students’ learning. Two fields of investigation are used: cooperative learning (Johnson &
Johnson, 2002) and social influence (Quiamzade & Mugny, 2001). Studies examined the
role of information distribution during dyadic cooperative work on texts: students worked
either on complementary information or on identical information. When students work on
complementary information, each student receives only one part of the needed information and accesses the other part through his/her partner. When students work on
identical information, students access the whole information before discussion. At each
session, students worked on two texts with the same partner. One student was asked to
summarize the text while the other asked questions or added comments. Roles were
reversed for the second text.
Results involving university students illustrate two different dynamics regarding
student interactions and learning. Working on complementary information produced
more positive interactions and more investment in information transmission. On the other
hand, working on identical information produced confrontation of points of view as well
as a focus on social comparison of competence. This comparison of competence was
detrimental for learning in that condition.
Results also underlined that information distribution moderates the relation between the representation of the partner and student learning. Partner’ competence (either assessed by questionnaire or manipulated thanks to a confederate) is welcomed
and positive for students’ learning when they worked on complementary information, but
is threatening for students’ own competence and detrimental for learning when they work
on identical information. A similar pattern was observed with primary pupils regarding the
relation between summarizers’ informational contribution (in terms of the amount of information) and their listeners’ learning, the relation is positive when pupils work on complementary information but negative when they work on identical information.
References
Johnson, D. W., & Johnson, R. T. (2002). Social interdependence theory and university
instruction: Theory into practice. Swiss Journal of Psychology, 61, 119–129.
Quiamzade, A., & Mugny, G. (2001). Social influence dynamics in aptitude tasks. Social
Psychology of Education, 4, 311–334.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Mutuality as the cream on the milk
Michael Baker
CNRS & Université Paris 10, UMR MODYCO, France
[email protected]
The somewhat exotic title of this paper is a modified quotation from the pragmadialectical theory of argumentative speech acts (van Eemeren & Grootendorst, 1984), that distinguishes the ‘cream’ of dialogical attitudes such as acceptance and conviction, from the
‘milk’ of more deep-seated underlying beliefs. One problem in modelling argumentation
dialogue is to understand the relationships between what is said and believed, in relation
to the manifested interpersonal commitments imposed by public language use.
The purpose of this paper is primarily theoretical and methodological: what is the
nature of mutual understanding in the case of collaborative problem-solving dialogues,
and what are the methodological consequences of a particular theoretical understanding
of mutuality in dialogues for understanding how students’ knowledge evolves in an by
them?
I begin from the postulate that dialogue is not a “window on the mind” (Edwards,
1993): what is expressed in a dialogue act is, by hypothesis, influenced by the interlocutor, during and before utterance; it is also conditioned by contextual constraints, such as
producing the most convincing argumentative defense. Explanation is a contextual reconstruction rather than a matter of rendering beliefs explicit. So what is expressed does
not necessarily reflect beliefs that were supposedly at the origin of problem solutions in
any direct way.
The best known proposal for mutuality is that of mutual belief on the meaning of
utterances, understood to a degree that is sufficient for the current purposes (Clark &
Schaefer, 1989). Such a “common ground” can be extended to cover the notions of intersubjectivity and the common cultural background (Baker, Hansen, Joiner, & Traum,
1999). Drawing on the work of Cohen (1992), I shall argue, firstly, that mutuality in such
cases is better understood as “acceptance” rather than “belief”, as a policy for joint reasoning. Secondly, with reference to models of negotiation in dialogue (e.g. Roulet, 1992;
Baker, 1994), I shall argue that dialogue acts functioning in co-elaboration of problem
solutions are better seen as conditional proposals (“I will accept if you will”) than as assertives (classically, i.e. Searle, 1969, expressions of justifiable belief). The ‘cement’ of
collaboration can thus be seen as doubly conditional acceptance: acceptance of the locuter is conditional of that of the interlocutor, and this in turn is conditional on acceptance on the way in which the content of the original proposal has been mutually
elaborated. This overall theoretical vision of mutuality enables some explanation of the
‘friability’ of students’ proposals in collaborative problem-solving: often, elaborate multiple solutions are built up, only to be dropped. It is thus only at key moments of this iterative and negotiative process that a glimpse of mutual beliefs can be obtained.
The above considerations will be discussed in reference to dialogue extracts from
a wide variety of collaborative problem-solving situations, including those involving CMC.
In conclusion, a complementary perspective on the fluctuation of mutuality in dialogue is
discussed, derived from complex systems theory. The ‘epistemic productivity’ of a dialogue can be understood in terms of a ‘growth factor’ of the ‘population’ of mutuality.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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References
Baker, M. J. (1994). A model for negotiation in teaching-learning dialogues. Journal of
Artificial Intelligence in Education, 5(2), 199-254.
Baker, M.J., Hansen, T., Joiner, R., & Traum, D. (1999). The role of grounding in collaborative learning tasks. In P. Dillenbourg (Ed.), Collaborative Learning: Cognitive and
Computational Approaches, pp. 31-63. Amsterdam : Pergamon / Elsevier Science.
Clark, H. H., & Schaefer, E. F. (1989). Contributing to discourse. Cognitive Science, 13,
259-294.
Cohen, J. (1992). An essay on belief and acceptance. Oxford University Press: Oxford
Edwards, D. (1993). But what do children really think?: Discourse analysis and conceptual content. In Children's Talk. Cognition and Instruction, 11(3-4), 207-225.
Roulet, E. (1992). On the structure of conversation as negotiation. In (On) Searle on
Conversation, pp. 91-100, (eds.) Parret, H. & Verschueren, J. Amsterdam : John
Benjamins.
Searle, J. (1969). Speech acts: An essay in the philosophy of language. Cambridge,
England: Cambridge University.
van Eemeren, F. H., & Grootendorst, R. (1984). Speech acts in argumentative discussions. A Theoretical model for the analysis of discussions directed towards solving
conflicts of opinion. Dordrecht/Cinnaminson: Foris Publications, PDA 1.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Analysis of Participants’ Computer-Mediated Distance Cooperation compared to their Own View of their Cooperation
Kristine Lund, Céline Rossetti, & Stéphanie Metz
ICAR, CNRS, University of Lyon 2, France
{Kristine.Lund, Celine.Rossetti, Stephanie.Metz}@univ-lyon2.fr
Using Baker’s model of cooperative problem-solving activity (Baker, 2002), we analyzed
how three dyads in a pilot study and ten dyads in a principal study cooperated at a distance to write instructions for folding an origami paper hen. We show how different forms
of cooperative activity — as defined by Baker’s model — are correlated with two factors
internal to the interaction between the individuals of the dyad (Lund, Rossetti, & Metz,
2006). Firstly, dialogue utterances between partners that have a dominant social aspect
are positively related to the symmetry of the entire dyadic interaction in terms of partners’
contributions. Secondly, dialogue utterances that predominantly deal with expressing
what partners are doing is negatively related to the extent to which partners are aligned
(in synch with each other).
In the pilot study, two of the dyads were interviewed as a team after participating.
In the principal study, eight individuals were interviewed separately and all twenty answered questionnaires. In this presentation, we will show — for a select number of dyads
— how our analysis of participants’ computer-mediated distance cooperation in terms of
Baker’s model (op. cit.), compared to their own view of their cooperation. Concerning the
pilot study, we will illustrate how the forms of cooperation that were a result of our analyses compared to the answers of two dyads concerning 1) dealing with a partner whose
identity is unknown and 2) the adoption of specific working procedures and/or strategies.
Concerning the principal study, we illustrate how the forms of cooperation that were a
result of our analyses compared to the answers of four individuals (two dyads) concerning these same two questions, but also regarding the roles established during activity
and the level of understanding within the dyad.
This initial comparison of our analyses of these four dyad’s cooperation vs. the
individuals’ views on their cooperation (two in the pilot study and two in the principal
study) will form the basis for hypothesis generation on modeling dyadic cooperation vs.
gaining insight through interviewing and questionnaires. Methodological implications for
mutual modeling will be discussed.
References
Baker, M. J. (2002). Forms of cooperation in dyadic problem-solving. Revue d'Intelligence Artificielle, 16(4-5), 587-620.
Lund, K., Rossetti, C., & Metz, S. (2006). Les facteurs internes à la coopération, influencent-ils l’activité médiatisée à distance? In M. Sidr, E. Bruillard, & G.-L. Baron
(Eds.), Actes des Premières Journées Communication et Apprentissage Instrumentés en Réseau JOCAIR '06, Université de Picardie Jules Vernes : Amiens, pp. 310329.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Position paper
Justine Cassell
Media Technology & Society, Northwestern University, USA
[email protected]
In the past ten years there has been increasing interest in the role played by the
body in discourse and dialogue. In this talk, I will describe a series of explorations
into the conversational functions of hand gestures, eye gaze, head movement
and posture. These explorations concentrate on the diverse ways that body behaviors contribute to collaborative dialogue---they offer interlocutors important
resources for describing the world, for coordinating their understanding of each
other's models of the world and of the conversation, and for establishing a sense
of mutual rapport. Each exploration also leads to an implemented embodied dialogue agent (virtual human) that generates natural language paired appropriately
with the nonverbal behavior.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Shared Visual Information as a Window into Other Minds
Darren Gergle
Northwestern University, USA
[email protected]
The focus of my research is to understand how various forms of shared visual information, of the sort often produced in collaborative technologies, influence the construction and maintenance of a model of a partner’s state of mind. Early evidence has
demonstrated that speakers and addressees take into account what one another can
see (Schober, 1993), and they notice where one another’s attention is focused (Boyle,
Anderson, & Newlands, 1994), during the construction and comprehension of utterances. Therefore, when technological mediation is introduced, we need to understand how it
can disrupt the mechanisms that typically support collocated interaction by allowing us to
maintain a model of our partner’s state.
Together with my colleagues, I have demonstrated three primary ways in which
shared visual information supports collaboration (Fussell, Kraut, Gergle, & Setlock,
2005). First, it supports inferences about a partner’s state of mind (e.g., their level of
comprehension or current focus of attention). Second, it informs inferences about their
knowledge and abilities (e.g., their degree of cognitive competence). Finally, it supports
inferences about the state of a joint task (e.g., how a task is proceeding towards an end
goal). Together these inferences help to shape the content and timing of contributions as
well as decisions speakers make about when to provide additional content or clarifications.
The approach I have taken in this work is to use a referential communication task
in which Helper/Worker pairs collaborate on the construction of a visual puzzle. This online data collection method requires a Helper to describe to a Worker how to construct a
puzzle so it matches a target configuration. This allows the capture of data in a number
of visual conditions. These data can then be explored to provide insight into the ways in
which shared visual information supports partner modeling.
Performance metrics from these studies capture basic statistics such as task
completion time and number of errors. More detailed measures of communicative efficiency uncover the ways in which the structure of communication changes on the basis
of the mutual knowledge established by the shared visual information. For example, the
presence of shared visual information impacts how partners distribute their initiative and
conversational effort, and mutual knowledge of a partner’s access to shared visual information allows the use of more efficient linguistic terms such as deictic expressions
(e.g., “this goes there”). Sequential analysis techniques reveal that pairs with a shared
workspace are less likely to explicitly verify their actions with speech. Rather, they rely
on visual information to provide the necessary communicative and coordinative cues for
the task (Gergle, Kraut, & Fussell, 2004). Finally, my recent work extends these findings
by developing a detailed process model that explicitly describes how shared visual information and linguistic information combine to account for the patterns of referring behavior observed in the puzzle studies. As I will discuss, this computationally explicit
method allows the exploration of a variety of hypothesized models of partner modeling
that could be used for the construction and comprehension of referring expressions in
shared visual environments.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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References
Boyle, E. A., Anderson, A. H., & Newlands, A. (1994). The effects of visibility on dialogue
and performance in a cooperative problem solving task. Language & Speech, 37(1),
1-20.
Fussell, S. R., Kraut, R. E., Gergle, D., & Setlock, L. D. (2005). Visual Cues as Evidence
of Others' Minds in Collaborative Physical Tasks. In B. F. Malle & S. D. Hodges
(Eds.), Other Minds: How Humans Bridge the Divide Between Self and Others (pp.
91-105). New York, NY: Guilford Press.
Gergle, D., Kraut, R. E., & Fussell, S. R. (2004). Action as language in a shared visual
space. ACM conference on Computer supported cooperative work (pp. 487-496).
Chicago, Illinois, USA: ACM Press.
Schober, M. F. (1993). Spatial perspective-taking in conversation. Cognition, 47, 1-24.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Memory-Based Contributions to
Conversational Common Ground
William S. Horton
Department of Psychology, Northwestern University, USA
[email protected]
During routine interactions, speakers’ utterances often show evidence for audience design. That is, speakers often appear to adjust aspects of their speech in response to the
communicative needs of particular addressees. Although such adjustments occur quite
frequently in many conversational contexts, the cognitive mechanisms that underlie audience design are not well understood. A central question concerns the extent to which
instances of audience design necessarily emerge on the basis of considerations of the
knowledge taken as shared between interlocutors, or their common ground. In general,
individuals are presumed to coordinate interactions on the basis of beliefs about common ground, but evidence is mixed regarding when and how this actually occurs. Traditional accounts presume that beliefs about common ground are derived by consulting
relatively explicit models of others’ knowledge, either as part of initial message planning
or via secondary monitoring processes. In contrast, Horton and Gerrig (2005) proposed
that important aspects of conversational common ground can emerge on the basis of
fundamental mechanisms of implicit memory. On this memory-based account, other individuals can become linked, or associated, to a wide range of related information, simply due to the manner in which episodic traces of social interactions are routinely encoded. Once these traces have been established, these same individuals, when encountered subsequently, then function as memory cues for the retrieval of entire patterns of
associated information. These partner-based cues are assumed to “resonate” in parallel
with the entire contents of long-term memory, resulting in the increased accessibility of
relevant associations. Information that is strongly associated with the current conversational partner is especially likely to become accessible within the time course necessary
to have an impact upon concurrent language processes. Crucially, it is this enhanced
accessibility that allows speakers to produce utterances that appear on the surface to be
formulated with respect to more explicit considerations of common ground. An important
consequence of this claim is that partner-relevant information can serve as a relatively
automatic constraint upon language processing, independent of particular communicative intentions—similar to constraint-based approaches proposed in other domains of
language processing. On this view, then, mutual modeling is not an absolutely necessary aspect of language use, although the products of these memory-based processes
presumably form the basis for more considered evaluations of common ground when
necessary. An ongoing challenge is to identify when and how relatively low-level cognitive processes can give speakers and addressees access to partner-relevant information
in a way that can account for more complex phenomena such as audience design. Theoretical models of language use and collaboration will benefit from a better understanding of the basic cognitive mechanisms that allow interlocutors to interact on the basis of
beliefs about conversational common ground.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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References
Horton, W. S., & Gerrig, R. J. (2005). Conversational common ground and memory processes in language production. Discourse Processes, 40, 1-35.
Horton, W. S., & Gerrig, R. J. (2005). The impact of memory demands on audience design during language production. Cognition, 96, 127-142.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Cognitive Load and Audience Design: Interference with Construction and Updating of an Adequate Partner Model
Christian Roßnagel
International University Bremen, Jacobs Center for Lifelong Learning and Institutional
Development, Germany
[email protected]
Analytically speaking, adapting utterances to one’s communication partner’s knowledge
lies at the heart of successful communication (cf. Schober, 1998). However, despite its
importance, such perspective-taking is not as unconditional as might be expected.
Speakers showed little audience design in several studies both with monological designs
(e.g., Buhl, 1996; Schober, 1995; Speck, 1993; Stamov Roßnagel, 1995, 2000) and with
expert-novice communication (e.g., Brünner, 1993; Hartog, 1996; Rambow, 2000). Cognitive load may account for diminished audience design: by default speakers plan their
utterances egocentrically and adjust them to their partner only if needed. Under conditions of cognitive load, the adjustment stage might be skipped (cf. Horton & Keysar,
1996), yielding utterances with virtually no partner adaptation. An explanation of cognitive load effects that has not yet been tested concerns the construction and updating of a
model of one’s communication partner. Cognitive load may interfere with storing and integrating information about one’s partner into a partner model, which would then fail to
inform partner-adapted speech planning.
Two experiments investigated the interaction of speakers’ construction and updating of partner models. In both experiments, participants worked on a model construction task under conditions of high or low cognitive load. High load was induced through a
vigilance secondary task. Partner adaptation was assessed through experts’ use of
technical terms and level of detail. Upon completing the task, speakers rated their partner’s perceived competency and changes in their partner perception. In Experiment 1,
speakers were given no information about their partner’s level of experience with regard
to the construction task. Partners (who were confederates) through their feedback signalled that they found the task hard and had difficulty following the speaker’s instruction
(Exp. 1a), or they signalled understanding and that they found the task easy (Exp. 1b). In
Experiment 2, partners were introduced to speakers as familiar with and experienced in
the construction task or as complete novices. During the task, “experienced” partners
signalled that they found the task difficult, whilst “novices” conveyed that they had no
difficulties understanding the instruction.
Results showed that in Exp. 1, speakers in high load conditions described their
partners’ competency less accurately and with lower confidence. Also, they indicated low
awareness for changes in their partner perception during the task. In line with previous
findings, they displayed lower partner adaptation. Similarly, in Exp. 2, speakers under
high load showed minimal partner adaptation. Moreover, there was virtually no change in
partner adaptation after model-incongruent feedback. Post-experimental cued recall interviews support the conclusion that rather than skipping adjustment to an existing partner model, speakers failed to construct and update, respectively, such a model.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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References
Buhl, H. M. (1996). Wissenserwerb und Raumreferenz. Tübingen: Niemayer.
Hartog, J. (1996). Das genetische Beratungsgespäch: Institutionalisierte Kommunikation
zwischen Experten and Nicht-Experten. [Genetic counseling: Institutionalized communication between experts and non-experts] Tübingen: Gunter Narr Verlag.
Horton, W. S., & Keysar, B. (1996). When do speakers take into account common
ground? Cognition, 59, 91-117.
Rambow, R. (2000). Experten-Laien-Kommunikation in der Architektur. Münster: Waxmann.
Roßnagel, C. (2000). Cognitive load and perspective-taking: applying the automaticcontrolled distinction to verbal communication. European Journal of Social Psychology, 30, 429-445.
Roßnagel, C. (1995). Übung und Hörerorientierung beim monologischen Instruieren. Zur
Differenzierung einer Grundannahme. Sprache & Kognition, 14, 16-26.
Schober, M. F. (1998). Different kinds of conversational perspective-taking. In S. R.
Fussell & R. J. Kreuz (Eds.), Social and cognitive psychological approaches to interpersonal communication. Mahwah, NJ: Lawrence Erlbaum.
Schober, M.F. (1995). Speakers, addressees, and frames of reference: Whose effort is
minimized in conversations about location? Discourse Processes, 20(2), 219-247.
Speck, A. (1993). Textproduktion im Dialog. Unveröffentlichte Dissertation, FU Berlin.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
22
Transactive Memory and Work Group Performance
Richard Moreland
University of Pittsburgh, Pennsylvania, USA
[email protected]
For several years, I have been studying transactive memory in small work
groups. Transactive memory is a shared awareness among members of who
knows what in the group. Groups with stronger transactive memory systems are
likely to perform better because (for example) they can make wiser assignments
of tasks to members, coordinate their members’ work more smoothly, and solve
any problems more quickly and efficiently. My research, all done in a laboratory
setting, has focused on identifying methods for encouraging the development of
transactive memory systems in newly-formed groups. Several such methods
(e.g., training group members together, rather than apart, so that each of them
can see direct evidence of what the others do and do not know) have now been
identified, and in each case, stronger transactive memory systems were indeed
associated with better group performance. I will review this work briefly in my
presentation, then focus on my most recent research, which explores whether the
development of transactive memory systems is an effortful or an automatic process.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
23
Investigating the Subjective and Situated Experience of
Mutual Modelling
Béatrice Cahour
National Centre of Scientific Research, IRIT/GRIC, France
[email protected]
Mutual modelling in a collaborative situation can be seen as a dynamic construction of
representations and feelings, more or less clear, that one person has relatively to his/her
collaborator. “Modelling” is then considered in an open sense. This modelling is partially
created during the collaborative situation in a highly situated way, depending largely on
the situational context, what occurs here and there, what the other person says, how she
says it, and how she moves, looks at you or others, draws a schema, etc. Since dependant on the situational context, it is dynamically evolutionary. It is partially motivated
by the collaboration, intentionally constructed to facilitate the cooperation, and partially
not intentional but built in a more spontaneous and automatic way, a part more difficult to
grasp because less conscious. These characteristics render the investigation of the mutual modelling methodologically complex.
How mutual modelling working is function of the situation. For this reason, it is
necessary to develop field studies to know how it works in such or such situation, and to
adapt the processes we have identified by generalization to the specific case we are interested in (and get new information in return).
From two or three different situations of collaboration that we have studied (collaboration between waiters in a restaurant; collaboration to buy a present remotely; collaboration to produce a story face to face), we will ask the following questions:
-
What is modelled by the subjects about the others in this situation?
-
How is it constructed by the subjects, from which type of cues in the situation?
-
How can we, as researchers, get information about the two previous points?
Which methodologies can be used to approach these phenomena?
We then show how, in each situation, the observable data gathered by video recording are sometimes sufficient to infer the mutual modelling (for example the modelling
of actions) but often not enough to infer what a subject is thinking about his collaborator
(which may be task-oriented or relation-oriented). Because the mutual modelling is a
subjective and internal process, we can only partially infer it from the observable behaviour displayed by the subjects. We then need methodologies to know about this subjective and internal construction of the model. We will call these methodologies “experiential
methodologies” since they allow us to get information about the lived experience of the
subject.
The risk of asking the subject to describe verbally his lived experience is that he
rationalizes it, explains it, justifies it, and then comment his activity instead of describing
what happened during the situation. Instead of that, we want the subject to be in a position of ‘embodied talk’, remembering what happened during the collaborative situation.
The more he will recall it precisely, the less he will reconstruct what happened and will
have a reliable recollection.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Two types of interviews seem appropriate to help the subjects remembering: (1)
the “explicitation” interview (in the psycho-phenomenological perspective of Vermersch),
and (2) the “self-confrontation” interview sometimes used in ergonomics, with a trace of
the activity (video, audio…) as a mnemonic support. Both types of interviews aim at resituating the subjects in the past activity of collaboration. The use of one or/and the other
depends on the available activity traces, on what was the nature of the activity (auditory,
visual, kinaesthetic…) and on the attention focus of the subject during the activity. The
specific techniques of these interviews to establish and maintain a state of evocation in
the interviewees and to prompt them (use of sensorial context, non-inducing questioning,
avoiding why-questions…) will be developed, as well as the advantages and limits of
each type of interviews (with and without traces).
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Suspending and Reinstating Collaborative Tasks:
Facework and Grounding
Eric Chevalley & Adrian Bangerter
Institut de Psychologie du Travail et des Organisations, University of Neuchâtel, Switzerland
{eric.chevalley, adrian.bangerter}@unine.ch
Interruptions of collaborative tasks such as conversations are common. Typically, interrupted persons must suspend the conversation, address the problem and then reinstate
the conversation. That process requires coordinating both interpersonal and task-related
processes between partners. In conversation, people commit to interact and to talk about
a topic. These commitments are established sequentially (Clark, 1996). Interrupted participants have to negotiate suspending and reinstating both the interaction and the topic.
This is face threatening (Brown & Levinson, 1987) and disrupts coordination and grounding (Clark, 1996).
We propose two studies exploring the joint nature of the suspension process. In
the first study, we examined naturally-occurring interruptions in telephone conversations.
Results showed that longer suspensions led to more face management (more extensive
requests, apologies and justifications were given) and more verbal effort to reinstate the
topic. Conversational role of the person interrupted (speaker or listener) also influenced
the suspension process. Listeners were more polite than speakers. In the second study,
we experimentally manipulated interruption length in telephone conversations. We induced short (M = 12 sec) vs long interruptions (M = 36 sec) through a cover story. Results showed that length of suspension had similar effect of on face management and on
effort to reinstate the conversation. Additionally, we found that metalanguage about the
reinstatement of the conservation (e.g., where was I?) were more frequent after long
suspensions. Conversational roles influenced the initiation of suspension. The effort to
suspend is greater for listeners than for speakers. However, speakers and listeners were
equally fast in reinstating the conversation.
Both series of results highlight the interactional costs of interrupting collaborative
tasks, an issue on which there is currently almost no research. Given the ubiquity of interruptions of collaborative tasks in everyday situations, more research is needed on
these processes.
References
Brown, P., & Levinson, S. (1987). Politeness: Some universals in language usage. Cambridge: Cambridge University Press
Clark, H. H. (1996). Using Language. Cambridge University Press.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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Evaluating Mutual Modeling in CSCW Environments
Nicolas Nova, Gaëlle Molinari, Pierre Dillenbourg
CRAFT, School of Computer Sciences and Communication, Ecole Polytechnique
Fédérale de Lausanne (EPFL), Switzerland
{nicolas.nova, gaelle.molinari, pierre.dillenbourg}@epfl.ch
“Mutual modeling” has been defined as the construction and the update of the representations individuals make of their partners' knowledge, beliefs, goals, needs and understanding. Even as a broad notion, this socio-cognitive process has been acknowledged
as being of peculiar importance to Computer Supported Collaborative Work and Learning research. Research about this very topic is geared towards two directions. On one
hand, from a descriptive point of view, researchers are trying to assess the existence of
mutual models (is it a continuous or a situated process?) or investigating what components they are made of (is it about grasping others’ intents? Knowledge? Beliefs?). On
the other hand, from the practitioner side, some scholars want to design environment
that would eventually enhance this process; namely by providing mutual awareness interfaces.
However, researchers from both sides face an important problem: the abstract
and unobservable aspect of mutual modeling raises methodological challenges regarding its apprehension. And the most important problem is not its broad definition that encompasses various possible contents that can be modeled (ranging from goals to the
understanding of the situation). The purpose of this paper is to describe the different
means I deployed to grasp mutual modeling in two studies I carried out.
In my research, I mostly focused on a sub-part of mutual modeling: how partners
model others’ intentions: about what they will be doing in the next minutes. Using two
computer games, one in virtual space, another in the physical environment, I studied
how different types of awareness interfaces might influence mutual modeling of partners’
intents.
To do so, I used various techniques ranging from in-task crossed questionnaires
to group interview after the game. The understanding of mutual modeling indeed necessitates choices such as a methodological posture (qualitative or quantitative enquiry), the
kind of data to be collected (participants’ answers or participants’ actions and behavior)
and the moment to apply them (in task or after the task). The two projects I will present
combined those methods. The paper will then present those techniques and discuss
their implications with regards to the results obtained. What this contribution would like to
stress is the fact that the apprehension of a complex process such as mutual modeling
needs a multi-method approach.
CSCL Alpine Rendez-Vous. Mutual Modeling Workshop. 22-23 January 2007.
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