The emergence of dynamic visualizations of three-dimensional (3D) models in anatomy curricula may be an adequate solution for spatial difficulties encountered with traditional static learning, as they provide direct visualization of change throughout the viewpoints. However, little research has explored the interplay between learning material presentation formats, spatial abilities, and anatomical tasks. First, to understand the cognitive challenges a novice learner would be faced with when first exposed to 3D anatomical content, a six-step cognitive task analysis was developed. Following this, an experimental study was conducted to explore how presentation formats (dynamic vs. static visualizations) support learning of functional anatomy, and affect subsequent anatomical tasks derived from the cognitive task analysis. A second aim was to investigate the interplay between spatial abilities (spatial visualization and spatial relation) and presentation formats when the functional anatomy of a 3D scapula and the associated shoulder flexion movement are learned. Findings showed no main effect of the presentation formats on performances, but revealed the predictive influence of spatial visualization and spatial relation abilities on performance. However, an interesting interaction between presentation formats and spatial relation ability for a specific anatomical task was found. This result highlighted the influence of presentation formats when spatial abilities are involved as well as the differentiated influence of spatial abilities on anatomical tasks.

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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

This paper studies learners' emotion awareness in university level academic contexts as a first step to help learners regulate their emotions. Existing emotion awareness tools offer little information on learners' emotions and their antecedents. This study created an emotion-reporting grid for university students based on the emotions they experienced daily. Students were interviewed based on their selfreported grid. A quantitative descriptive analysis of these retrospective interviews was conducted based on Pekrun's control-value theory of achievement emotions. Student transcripts were analyzed based on the focus of their emotions (retrospective, activity, or prospective), the causes they attribute to their emotions (agent or external circumstances) and how they appraised the situation in which they experienced the emotions (value and control). We discuss the results with regard to the types of emotion-oriented and appraisal-oriented regulation strategies used in learning contexts and draw implications for the design of emotion awareness tools to support emotion regulation processes.

International audienceThe objective of the research presented here was to study the influence of two types of instruction for using an argumentation diagram during pedagogical debates over the Internet. In particular, we studied how using an argumentation diagram as a medium of debate (“Graph for debating”) compared to using an argumentation diagram as a way of representing a debate (“Graph for representing chat debate”). Two groups of students produced an individual argument diagram, then debated in pairs in one of the two conditions, and finally revised their individual diagrams in light of their debate. We developed an original analysis method (ADAM) in order to evaluate the differences between the argumentation diagrams constructed collaboratively, during the interactions that constituted the experimental conditions, as well as individually before and after debate in the two experimental conditions. The results suggest a complimentary relationship between the two types of argumentation diagram usage in the framework of conceptual learning centered on issues of debate important for society. More specifically and firstly, students who were instructed to use the argumentation diagram to represent their debate (“Graph for representing chat debate”) were less inclined to take position in relation to the same graphical element while collaborating. On the other hand, students who were instructed to use the argumentation diagram alongside a chat (“Graph for debating”) expressed more personal opinions while collaborating. Secondly, the instructions given to the participants regarding the use of the argumentation diagram during the collaborative phase (either for debate or for representing a chat debate) have a significant impact on the post-individual graphs. In the individual graphs revised after the collaborative phase, participants in the condition “graph for representing chat debate” added more examples, consequences and causes. It follows that a specific usage for an argumentation diagram can be chosen and instructions given, based on pedagogical objectives for a given learning situation

Abstract-In this paper we propose a method to assess key collaborative processes during computer-supported group work based on physiological signals and eye-movements. Synchronous interpersonal multimodal signals from 30 dyads were recorded while collaborating remotely. Features measuring how much collaborators' eye-movements and physiology are coupled were extracted from the obtained time series and two regression models were trained to assess collaboration. Results show that the two coupling measures can be used to predict collaborative processes such as emotion management and convergence. Assessing those processes is a major step toward the development of remote collaborative interfaces able to adapt to the users' social interactions.

The present study is part of a project aiming at empirically investigating the process of modeling the partner's knowledge (Mutual Knowledge Modeling or MKM) in Computer-Supported Collaborative Learning (CSCL) settings. In this study, a macro-collaborative script was used to produce knowledge interdependence (KI) among colearners by providing them with different but complementary information. Prior to collaboration, two students read the same text in the "Same Information" (SI) condition while each of them read one of two complementary texts in the "Complementary Information" (CI) condition. After the collaboration phase, a knowledge modeling questionnaire asked participants to estimate both their own -and their partner's outcome knowledge thanks to Likert-type scales. The relation between the accuracy with which co-learners assess their partner's knowledge and learning has been examined. In addition, we investigated the KI effect on (a) learning performance and (b) the MKM accuracy. Finally, we wondered to what extent the MKM accuracy could mediate the KI effect on learning. Results showed no difference in learning performance between participants who worked on same information and participants who worked on complementary information. We also found that participants were more accurate at assessing their partner's knowledge in the SI condition than in the CI condition. The discussion focuses on methodological limitations and provides new directions for investigating the KI effect on MKM accuracy.

Collaborative problem-solving has been gaining attention as more and more students and employees work together all around the world to find solutions to complex problems. This trend goes hand in hand with a growing interest in the role of affective processes in learning and problem-solving fields. However, the comprehension of real-time dynamics between emotional sharing and collaborative exchanges (what we propose to call "collaborative act") still needs to be deepened. The challenge is especially on understanding the interplay between real-time changes in epistemic and relational dimensions. In this study, we propose to explore this question in dyadic creative problem-solving. Eleven pairs of participants used an argument graph tool to co-create a slogan against violence at school. The tool was used to write down slogans and build a joint map of the group argumentation. During the collaboration, they had access to an emotion awareness tool, allowing them to share emotional labels in real time. An indicator of real-time use was computed to track ongoing changes in collaborative acts during collaboration. Then, using both inferential and descriptive statistics, we first investigated whether emotional sharing induces real-time adaptation of both emitter's and receiver's collaborative acts. Second, we looked at privileged relationships between emitter's collaborative acts, emitter's emotion sharing, and receiver's collaborative acts. The preliminary results obtained (1) confirm that emotional sharing regulates emitter's and receiver's collaborative acts and (2) strongly suggest that specific emotions mark specific patterns of collaboration in different collaborative phases, implying both the epistemic and the relational spaces of collaboration. These results highlight the value of studying emotional sharing for a deeper comprehension of the factors regulating collaborative problem-solving. Perspectives in educational psychology and computer science are considered, with the will to understand and promote better self-and co-regulation of collaborative problem-solving through emotional sharing.