Who Did What to Succeed? Individual Differences in Which Learning Behaviors Are Linked to Achievement

It is commonly assumed that digital learning environments such<br/>as intelligent tutoring systems facilitate learning and positively<br/>impact achievement. This study explores how different groups of<br/>students exhibit distinct relationships between learning behaviors<br/>and academic achievement in an intelligent tutoring system for<br/>English as a foreign language. We examined whether these differences are linked to students’ prior knowledge, personality traits, and motivation. We collected behavioral trace data from 507 German seventh-grade students during the 2021/22 school year and applied machine learning models to predict English performance based on learning behaviors (best-performing model’s 2 = .41). To understand the impact of specific behaviors, we applied the explainable AI method SHAP and identified three student clusters with distinct learning behavior patterns. Subsequent analyses revealed that these clusters also varied in prior knowledge and motivation: one with high prior knowledge and average motivation, another with low prior knowledge and average motivation, and a third with both low prior knowledge and low motivation. Our findings suggest that learning behaviors are linked differently to academic success across students and are closely tied to their prior knowledge and motivation. This hints towards the importance of personalizing learning systems to support individual learning needs better.<br/>