Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are first grouped into clusters. Then, an…

Students in general chemistry were partitioned into three groups by cluster analysis of six affective characteristics (emotional satisfaction, intellectual accessibility, chemistry self-concept, math self-concept, self-efficacy, and test anxiety). The at-home study strategies for exam preparation and in-class learning strategies differed among the…