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.
This paper presents an algorithm that detects misunderstandings in collaborative work at a distance. It analyses the movements of collaborators' eyes on the shared workspace, their utterances containing references about this workspace, and the availability of 'remote' deictic gestures. This method is based on two findings: 1. participants look at the points they are talking about in their message; 2. their gazes are more dense around these points compared to other random looks in the same timeframe. The algorithm associates the distance between the gazes of the emitter and gazes of the receiver of a message with the probability that the recipient did not understand the message.
Using dual eye-tracking to unveil coordination and expertise in collaborative Tetris The use of dual eye-tracking is investigated in a collabora tive game setting. The automatic collection of information about partner's gaze will eventually serve to build adaptive interfaces. Following this agenda, and in order to identify stable gaze patterns, we investigate the impact of social and task related context upon individual gaze and action during a collaborative Tetris game. Results show that experts as well as novices adapt their playing style when interacting in mixed ability pairs. We also present machine learning results about the prediction of player's social context.
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