Who Fail to Achieve Mastery in Computer-based Learning Environments?

Virtual Brief Paper ID: 55135
  1. aaa
    Seoyeon Park
    Texas A&M University

Abstract: Some students fail to get mastery even though they spend a considerable amount of time practicing a skill in computer-based learning environments (CBLEs). Thus, the purpose of this study is to build a simplified and fast prediction model for students’ unproductive failure in CBLEs. We used three variables that are available in most CBLEs: student performance, hints usage, and response time. Results showed that the detector with logistic regression using three variables shows high accuracy. Specifically, students’ performance on each trial and response time have statistically significant prediction power on students’ unproductive failure. However, hint usage was not the statistically significant predictor of predicting unproductive failure controlling for the other variables.


Conference attendees are able to comment on papers, view the full text and slides, and attend live presentations. If you are an attendee, please login to get full access.