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A key affordance of game-based learning environments is their potential to unobtrusively assess student learning without interfering with gameplay. In this paper, we introduce a temporal analytics framework for stealth assessment that analyzes students' problem-solving strategies. The strategy-based temporal analytic framework uses long short-term memory network-based evidence models and clusters sequences of students' problem-solving behaviors across consecutive tasks. We investigate this strategy based temporal analytics framework on a dataset of problem solving behaviors collected from student interactions with a game-based learning environment for middle school computational thinking. The results of an evaluation indicate that the strategy-based temporal analytics framework significantly outperforms competitive baseline models with respect to stealth assessment predictive accuracy.
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A Multi-View Predictive Student Modeling Framework with Interpretable Causal Graph Discovery for Collaborative Learning Analytics
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.
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- Award ID(s):
- 2112635
- PAR ID:
- 10660257
- Editor(s):
- Mills, Caitlin; Alexandron, Giora; Taibi, Davide; Lo_Bosco, Giosuè; Paquette, Luc
- Publisher / Repository:
- International Educational Data Mining Society
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.5281/zenodo.15870161
