“Labelling human emotional behaviour for improving Collaborative e-Learning”
Marta Arguedas & Thanasis Daradoumis & Fatos Xhafa
eLearn Center
Open University of Catalonia, Barcelona, Spain
BACKGROUND:
In a collaborative e-learning environment, students are usually involved in learning
activities such as collaborative writing (e.g., an essay in Wiki form) and debates in the
form of a chat or asynchronous forum.
Extended Activity Theory Scenario (that includes
emotional information and time factor)
Identifying students’ feelings and emotional states enclosed in such textual information
is critical, since sentiments can influence individual behavior and consequently group
dynamics as well as individual and group performance. For this reason, emotion
awareness becomes an important issue for both tutors and students themselves.
Especially for tutors, if they have as accurate affective information as possible about
the emotional state that every individual and group is encountered, they might be able
to provide more effective feedback to the students that includes not only cognitive but
also affective scaffold.
To this end, we need to define a global context-aware Emotion Analysis framework
(Daradoumis et al, 2013), which we base on an extension of the Activity Theory (AT)
(Engeström et al, 1999), that will allow us to analyze students’ collaborative learning
interactions in the above-mentioned virtual learning spaces (wiki, chat and forum)
from an emotional point of view, as shown Figure, on the right.
A first important step to this endeavor is to develop an approach that enables labeling
affective behavior and then presenting the emotional information to all the agents
involved in the learning process.
METHOD:
Our discourse analysis method, which is described in five layers,
incorporates non-intrusive automatic detection and extraction of emotions
from student-created texts.
Concerning the tools we employ, we first apply the dialogic RST model of
Daradoumis (1995) to annotate the role (nucleus or satellite) that each
move plays in the exchange structure.
Then, we combine it with our sentiment analysis method for showing what
kinds of emotions are explicit in text, their valence (positive or negative)
and as well as their degree of activation.
Finally, we also apply a further extension of RST to show the emotional
and cognitive structure of the discourse, by displaying the role that each
move plays in the exchange structure together with each emotion
obtained, as shown Figure.
OBJECTIVES:
Our research questions:
Goal
Our research aims at
Our study aims to investigate the
effectiveness of the emotion labeling
model to detect emotions in educational
discourse (text and conversation) in a
non-intrusive way making emotion
awareness explicit both at individual and
group level.
Hypothesis
Students who learn in a virtual
environment endowed by our emotion
labeling model improve motivation,
engagement and learning achievements.
O1: identifying the emotions that
students experience during their
collaborative learning activities,
O2: exploring the ways these emotions
influence and affect learning experience,
O3: building explicit emotion awareness
both at individual and group level,
O4: providing adequate and effective
cognitive and affective feedback to the
students
R1 Are there notable correlations between making
emotion awareness explicit both at individual and group
level and motivation of students who learn with our
emotion labelling model and without it?
R2 Are there notable correlations between making
emotion awareness explicit both at individual and group
level and engagement of students who learn with our
emotion labelling model and without it?
R3 Are there notable correlations between making
emotion awareness explicit both at individual and group
level and learning achievements of students who learn
with our emotion labelling model and without it?
R4 Is there a significant difference between the
expression of attitude states, behaviours and emotional
states of students who learn with our emotion labelling
model and without it?
EXPECTED RESULTS:
*detect the influence of learning context (group dynamics, working spaces and conditions, time, interactions) on
students’ emotional state.
* identify which parts of this context concern the 'cognitive' properties of students, such as their goals, beliefs,
knowledge and opinions and which parts of it concern their emotions.
* try to build those contexts (such as creating and resolving cognitive dissonance) that have an ultimate positive
impact both on students’ motivation for learning and on their emotional state, which in turn yields to a better
learning performance.