  In category learning, a growing body of literature has increasingly focused
on exploring the impacts of interleaving in contrast to blocking. The
sequential attention hypothesis posits that interleaving draws attention to the
differences between categories while blocking directs attention toward
similarities within categories. Although a recent study underscores the joint
influence of memory and attentional factors on sequencing effects, there
remains a scarcity of effective computational models integrating both
attentional and memory considerations to comprehensively understand the effect
of training sequences on students' performance. This study introduces a novel
integration of attentional factors and spacing into the logistic knowledge
tracing (LKT) models to monitor students' performance across different training
sequences (interleaving and blocking). Attentional factors were incorporated by
recording the counts of comparisons between adjacent trials, considering
whether they belong to the same or different category. Several features were
employed to account for temporal spacing. We used cross-validations to test the
model fit and predictions on the learning session and posttest. Our findings
reveal that incorporating both attentional factors and spacing features in the
Additive Factors Model (AFM) significantly enhances its capacity to capture the
effects of interleaving and blocking and demonstrates superior predictive
accuracy for students' learning outcomes. By bridging the gap between
attentional factors and memory processes, our computational approach offers a
more comprehensive framework for understanding and predicting category learning
outcomes in educational settings.
