Learning Analytics Dashboard for Problem-based Learning

Abstract

This study examined two machine learning models for de- signing a learning analytics dashboard to assist teachers in facilitating problem-based learning. Specifically, we used BERT to automatically process a large amount of textual data to understand students' scientific argumentation. We then used Hidden Markov Model (HMM) to find students' cognitive state transition with time-series data. Preliminary results showed the models achieved high accuracy and were coherent with related theories, indicating the models can provide teachers with interpretable information to identify in-need students.