A Multi-View Predictive Student Modeling Framework with Interpretable Causal Graph Discovery for Collaborative Learning Analytics
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| Title: | A Multi-View Predictive Student Modeling Framework with Interpretable Causal Graph Discovery for Collaborative Learning Analytics |
|---|---|
| Language: | English |
| Authors: | Halim Acosta, Seung Lee, Daeun Hong, Wookhee Min, Bradford Mott, Cindy Hmelo-Silver, James Lester |
| Source: | International Educational Data Mining Society. 2025. |
| Availability: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Peer Reviewed: | Y |
| Page Count: | 13 |
| Publication Date: | 2025 |
| 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 1561655 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Junior High Schools Middle Schools Secondary Education |
| Descriptors: | Learning Analytics, Cooperative Learning, Student Behavior, Prediction, Game Based Learning, Causal Models, Behavior Patterns, Middle School Students, Artificial Intelligence |
| Abstract: | Understanding the relationship between student behaviors and learning outcomes is crucial for designing effective collaborative learning environments. However, collaborative learning analytics poses significant challenges, not only due to the complex interplay between collaborative problem-solving and collaborative dialogue but also due to the need for model interpretability. To address these challenges, this paper introduces a multi-view predictive student modeling framework using causal graph discovery. We first extract interpretable behavioral features from students' collaborative dialogue data and game trace logs to predict student learning within a collaborative game-based learning environment. We then apply constraint-based sequential pattern mining to identify cognitive and social behavioral patterns in student's data to improve predictive power. We employ unified causal modeling for interpreting model outputs, using causal discovery methods to reveal key interactions among student behaviors that significantly contribute to predicting learning outcomes and identifying frequent collaborative problem-solving skills. Evaluations of the predictive student modeling framework show that combining features from dialogue and in-game behaviors improves the prediction of student learning gains. The findings highlight the potential of multi-view behavioral data and causal analysis to improve both the effectiveness and the interpretability of collaborative learning analytics. [For the complete proceedings, see ED675583.] |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | ED675625 |
| Database: | ERIC |
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