Multimodal Late Fusion Model for Problem-Solving Strategy Classification in a Machine Learning Game
This work addresses the need for more accurate strategy-sensitive assessment and adaptive support in interactive learning contexts, though it is incremental as it builds on existing multimodal fusion techniques.
The paper tackled the problem of classifying students' problem-solving strategies in a digital learning environment by proposing a multimodal late fusion model that integrates screencast-based visual data and structured in-game action sequences, resulting in a classification accuracy increase of over 15% compared to unimodal baselines in a pilot study with 149 secondary school students.
Machine learning models are widely used to support stealth assessment in digital learning environments. Existing approaches typically rely on abstracted gameplay log data, which may overlook subtle behavioral cues linked to learners' cognitive strategies. This paper proposes a multimodal late fusion model that integrates screencast-based visual data and structured in-game action sequences to classify students' problem-solving strategies. In a pilot study with secondary school students (N=149) playing a multitouch educational game, the fusion model outperformed unimodal baseline models, increasing classification accuracy by over 15%. Results highlight the potential of multimodal ML for strategy-sensitive assessment and adaptive support in interactive learning contexts.