Towards Adaptive Learning: A Bayesian Knowledge Tracing Approach to Student Skill Prediction Bayesian Knowledge Tracing for Modeling Daily Living Skills in Children with ASD

Abstract

Autism Spectrum Disorder (ASD) presents challenges in mastering Activities of Daily Living (ADLs), which are essential for independence. This study applies Bayesian Knowledge Tracing (BKT) to model the mastery of five ADL skills—eating, dressing, toothbrushing, combing, and bathing—using data from 27 learners (1,350 responses). BKT parameters, including initial mastery, learning transition, guessing, and slipping, were used to estimate individual learning trajectories. Results showed that eating was the easiest skill (predicted mastery = 0.78), while bathing and combing were the most difficult (<0.55). The model achieved an overall accuracy of 0.62, with strong detection of actual mastery (TP = 722) but a high false-positive rate (FP = 429), indicating sensitivity to the guessing parameter. Learning curves and heatmaps revealed substantial inter-student variability. A comparative evaluation with the Performance Factors Analysis (PFA) model showed that BKT achieved higher overall predictive accuracy (BKT = 0.6356; PFA = 0.5917), while PFA demonstrated a higher AUC (0.6747) but exhibited strong positive-class bias in classification. These findings demonstrate the usefulness of BKT in modeling ADL development and highlight its potential for adaptive learning systems that support personalized interventions for ASD learners.

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