Multimodal Learning Analytics for Predicting Student Collaboration Satisfaction in Collaborative Game-Based Learning
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| Title: | Multimodal Learning Analytics for Predicting Student Collaboration Satisfaction in Collaborative Game-Based Learning |
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| Language: | English |
| Authors: | Halim Acosta, Seung Lee, Bradford Mott, Haesol Bae, Krista Glazewski, Cindy Hmelo-Silver, James Lester |
| Source: | International Educational Data Mining Society. 2024. |
| Availability: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Peer Reviewed: | Y |
| Page Count: | 12 |
| Publication Date: | 2024 |
| Sponsoring Agency: | National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL) National Science Foundation (NSF), Division of Information and Intelligent Systems (IIS) National Science Foundation (NSF), Division of Social and Economic Sciences (SES) |
| Contract Number: | 2112635 1839966 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Junior High Schools Middle Schools Secondary Education |
| Descriptors: | Cooperative Learning, Game Based Learning, Learning Analytics, Prediction, Student Satisfaction, Middle School Students, Foreign Countries, Technology Uses in Education, Computer Games, Educational Games |
| Geographic Terms: | Philippines |
| Abstract: | Collaborative game-based learning offers opportunities for students to participate in small group learning experiences that foster knowledge sharing, problem solving, and engagement. Student satisfaction with their collaborative experiences plays a pivotal role in shaping positive learning outcomes and is a critical factor in group success during learning. Gauging students' satisfaction within collaborative learning contexts can offer insights into student engagement and participation levels while affording practitioners the ability to provide targeted interventions or scaffolding. In this paper, we propose a framework for inferring student collaboration satisfaction with multimodal learning analytics from collaborative interactions. Utilizing multimodal data collected from 50 middle school students engaged in collaborative game-based learning, we predict student collaboration satisfaction. We first evaluate the performance of baseline models on individual modalities for insight into which modalities are most informative. We then devise a multimodal deep learning model that leverages a cross-attention mechanism to attend to salient information across modalities to enhance collaboration satisfaction prediction. Finally, we conduct ablation and feature importance analysis to understand which combination of modalities and features is most effective. Findings indicate that various combinations of data sources are highly beneficial for student collaboration satisfaction prediction. [For the complete proceedings, see ED675485.] |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | ED675589 |
| Database: | ERIC |
| Abstract: | Collaborative game-based learning offers opportunities for students to participate in small group learning experiences that foster knowledge sharing, problem solving, and engagement. Student satisfaction with their collaborative experiences plays a pivotal role in shaping positive learning outcomes and is a critical factor in group success during learning. Gauging students' satisfaction within collaborative learning contexts can offer insights into student engagement and participation levels while affording practitioners the ability to provide targeted interventions or scaffolding. In this paper, we propose a framework for inferring student collaboration satisfaction with multimodal learning analytics from collaborative interactions. Utilizing multimodal data collected from 50 middle school students engaged in collaborative game-based learning, we predict student collaboration satisfaction. We first evaluate the performance of baseline models on individual modalities for insight into which modalities are most informative. We then devise a multimodal deep learning model that leverages a cross-attention mechanism to attend to salient information across modalities to enhance collaboration satisfaction prediction. Finally, we conduct ablation and feature importance analysis to understand which combination of modalities and features is most effective. Findings indicate that various combinations of data sources are highly beneficial for student collaboration satisfaction prediction. [For the complete proceedings, see ED675485.] |
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