Multimodal Late Fusion Model for Problem-Solving Strategy Classification in a Machine Learning Game
By: Clemens Witt , Thiemo Leonhardt , Nadine Bergner and more
Potential Business Impact:
Helps games guess how students learn best.
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.
Similar Papers
Exploring Fusion Strategies for Multimodal Vision-Language Systems
Machine Learning (CS)
Makes AI faster by mixing pictures and words early.
Rethinking the Potential of Multimodality in Collaborative Problem Solving Diagnosis with Large Language Models
Computation and Language
Helps computers understand how students work together.
Meta Fusion: A Unified Framework For Multimodality Fusion with Mutual Learning
Machine Learning (CS)
Combines different data to make better predictions.