It is not either or: An initial investigation into combining collaborative and individual learning using an ITS

Jennifer Kaitlyn Olsen
2019
Article

Résumé

Research on Computer-Supported Collaborative Learning (CSCL) has provided significant insights into why collaborative learning is effective and how we can effectively provide support for it. Building on this knowledge, we can investigate when collaboration is beneficial to support learning. Specifically, collaborative and individual learning are often combined in the classroom, and it is important for the CSCL community to understand when a combination is beneficial compared to individual or collaborative learning alone. Before investing significant work into discovering these details, an initial investigation is needed to determine if there may be any value in a combination. In this study, we compared a combined condition to individual or collaborative-only learning conditions using an intelligent tutoring system for fractions. The study was conducted with 382 4th and 5th grade students. Students across all three conditions had significant learning gains, but the combined condition had higher learning gains than the other conditions. However, this difference was restricted to the 4th grade students. By analyzing the hints and errors of students over time from process data, we found that students in the combined condition tended to make fewer errors both when working collaboratively and individually, and asked for fewer hints than the students in the other conditions. Students who collaborated (collaborative and combined conditions) also reported having higher situational interest in the activity. By finding support for the effectiveness of combining collaborative and individual learning, this paper opens a broader line of inquiry into how they can effectively be combined to support learning.

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https://infoscience.epfl.ch/record/272334?ln=fr

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Jennifer Kaitlyn Olsen
2019
Article

Résumé

Official source

https://infoscience.epfl.ch/record/272334?ln=fr

À propos de ce résultat

Concepts associés (11)

Concepts associés

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Publications associées (9)

Publications associées

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Temporal analysis of multimodal data to predict collaborative learning outcomes

Jennifer Kaitlyn Olsen, Kshitij Sharma

The analysis of multiple data streams is a long-standing practice within educational research. Both multimodal data analysis and temporal analysis have been applied successfully, but in the area of collaborative learning, very few studies have investigated specific advantages of multiple modalities versus a single modality, especially combined with temporal analysis. In this paper, we investigate how both the use of multimodal data and moving from averages and counts to temporal aspects in a collaborative setting provides a better prediction of learning gains. To address these questions, we analyze multimodal data collected from 25 9-11-year-old dyads using a fractions intelligent tutoring system. Assessing the relation of dual gaze, tutor log, audio and dialog data to students' learning gains, we find that a combination of modalities, especially those at a smaller time scale, such as gaze and audio, provides a more accurate prediction of learning gains than models with a single modality. Our work contributes to the understanding of how analyzing multimodal data in temporal manner provides additional information around the collaborative learning process.

WILEY2020

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Knowledge interdependence with the partner, accuracy of mutual knowledge model and computer-supported collaborative learning

Pierre Dillenbourg, Gaëlle Molinari, Mirweis Sangin

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.

2009

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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 learning.

2010

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