Learning from animation enabled by collaboration

Animated graphics are extensively used in multimedia instructions explaining how natural or artificial dynamic systems work. As animation directly depicts spatial changes over time, it is legitimate to believe that animated graphics will improve comprehension over static graphics. However, the research failed to find clear evidence in favour of animation. Animation may also be used to promote interactions in computer-supported collaborative learning. In this setting as well, the empirical studies have not confirmed the benefits that one could intuitively expect from the use of animation. One explanation is that multimedia, including animated graphics, challenges human processing capacities, and in particular imposes a substantial working memory load. We designed an experimental study involving three between-subjects factors: the type of multimedia instruction (with static or animated graphics), the presence of snapshots of critical steps of the system (with or without snapshots) and the learning setting (individual or collaborative). The findings indicate that animation was overall beneficial to retention, while for transfer, only learners studying collaboratively benefited from animated over static graphics. Contrary to our expectations, the snapshots were marginally beneficial to learners studying individually and significantly detrimental to learners studying in dyads. The results are discussed within the multimedia comprehension framework in order to propose the conditions under which animation can benefit to learnin

Collaboration and abstract representations: towards predictive models based on raw speech and eye-tracking data

This study aims to explore the possibility of using machine learning techniques to build predictive models of performance in collaborative induction tasks. More specifically, we explored how signal-level data, like eye-gaze data and raw speech may be used to build such models. The results show that such low level features have effectively some potential to predict performance in such tasks. Implications for future applications design are shortly discussed

Knowing What the Peer Knows: The Differential Effect of Knowledge Awareness on Collaborative Learning Performance of Asymmetric Pairs

In an empirical study, we provided (or not) pairs of students working in a remote collaborative learning situation with a knowledge awareness tool that provided learner A with learner B’s level of knowledge measured through a pre-test. We analyzed the effect of the knowledge awareness tool on asymmetric pairs with regards to the prior-knowledge. Post-hoc analysis on the pairs’ knowledge level showed that the knowledge awareness tool mainly affects the learning performances of asymmetric pairs. Further analysis on the learners’ level showed that the knowledge awareness tool mainly affects the collaborative learning gain of the more-knowledgeable peers of asymmetric pairs. The results are discussed in light of socio-cognitive processes such as audience design and perspective taking

The Symmetry of Partner Modelling

© 2016, International Society of the Learning Sciences, Inc. Collaborative learning has often been associated with the construction of a shared understanding of the situation at hand. The psycholinguistics mechanisms at work while establishing common grounds are the object of scientific controversy. We postulate that collaborative tasks require some level of mutual modelling, i.e. that each partner needs some model of what the other partners know/want/intend at a given time. We use the term “some model” to stress the fact that this model is not necessarily detailed or complete, but that we acquire some representations of the persons we interact with. The question we address is: Does the quality of the partner model depend upon the modeler’s ability to represent his or her partner? Upon the modelee’s ability to make his state clear to the modeler? Or rather, upon the quality of their interactions? We address this question by comparing the respective accuracies of the models built by different team members. We report on 5 experiments on collaborative problem solving or collaborative learning that vary in terms of tasks (how important it is to build an accurate model) and settings (how difficult it is to build an accurate model). In 4 studies, the accuracy of the model that A built about B was correlated with the accuracy of the model that B built about A, which seems to imply that the quality of interactions matters more than individual abilities when building mutual models. However, these findings do not rule out the fact that individual abilities also contribute to the quality of modelling process

Peer knowledge modeling in computer supported collaborative learning

Learners benefit from collaboration because it triggers effective interaction processes such as externalization, elicitation and negotiation of knowledge. In order to communicate effectively, learners need to have a certain representation of their peers' knowledge. We refer to the process of building and maintaining a representation of the peers' knowledge as peer knowledge modeling. The present thesis aim at making contributions to the fields of computer support collaborative learning and work (CSCL, CSCW) on three main levels. First, as an empirical contribution, we investigate the process of peer knowledge modeling in the context of CSCL. Our main research question inquires the effects of a socio-cognitive support, providing co-learners with cues about their peer's prior knowledge, on collaborative learning outcomes and processes. In an empirical study (the KAT experiment), university students (N=64) participated in a remote computer-mediated dyadic learning scenario. They were provided (or not) with a visual representation of their partner's prior-knowledge level through a Knowledge Awareness Tool (KAT). Results showed that the KAT enhances co-learners' collaborative learning gain. This effect appears to be mediated by the positive effect of the KAT on participants' accuracy in estimating their peer's knowledge. Analyses on the process level showed that participants of the KAT condition produce more elaborated utterances. KAT condition dyads' interactions are more focused on knowledge negotiation, whereas the control condition dyads are mainly focused on task completion. The KAT seems to provide a sensitizing metacognitive support, structuring and regulating the collaboration by helping co-learners to cope with their knowledge gaps and discrepancies. Second, as a methodological contribution, we examine the affordance of dual eye tracking techniques as an innovative methodology to investigate, on a deeper level, the socio-cognitive processes underlying collaboration. We introduce DUET (DUal Eye-Tracking), a method using a multimodal technique to collect rich data featuring peers' synchronized gaze patterns, verbal interaction and potentially activities. We examine the main research applications of DUET and exemplify them with analyses conducted in the context of the KAT experiment. DUET method appears to be a promising technique to investigate collaborative processes on a deep level. Finally, as a third computational contribution, we built upon micro-level analyses of the verbal referencing process to introduce and test REGARD, a computational model allowing to automatically detect verbal references and locate the specific object of reference. The results of the test show a reasonably good accuracy of the REGARD algorithm to detect and associate verbal references to objects of reference. We discuss the design and research application of the REGARD model for the field of CSCL, CSCW and more generally HCI

Facilitating peer knowledge modeling: Effects of a knowledge awareness tool on collaborative learning outcomes and processes

We report an empirical study where we investigated the effects, on the collaborative outcomes and processes, of a cognition-related awareness tool providing learners with cues about their peer’s level of prior knowledge. Sixty-four university students participated in a remote computer-mediated dyadic learning scenario. Co-learners were provided (or not) with a visual representation of their peer’s level of prior knowledge through what we refer to as a knowledge awareness tool (KAT). The results show that, providing co-learners with objective cues about the level of their peer’s prior knowledge positively impacts learning outcomes. In addition, this effect seems to be mediated by the fact that co-learners provided with these objective cues become more accurate in estimating their partner’s knowledge – accuracy that predicts higher outcomes. Analyses on the process level of the verbal interactions indicate that the KAT seems to sensitize co-learners to the fragile nature of their partner’s as well as their own prior knowledge. The beneficial effect of the KAT seems to mainly rely on this induction of epistemic uncertainty that implicitly triggers compensation socio-cognitive strategies; strategies that appear to be beneficial to the learning process