Pre-college Characteristics and Online Homework Learning: Factors Associated with First Year Engineering Students’ Academic Success

The purpose of the study was to develop a working model to predict at risk students in an Introduction to Engineering course. The model considers both students’ pre-college characteristics, psychological traits, and online homework learning behavior. The study assisted the course instructor in the creation of an early warning system and the development of targeted interventions for students at risk. A reliable and valid instrument to measure engineering students’ pre-college characteristics was initially developed. The study also applied data mining to analyze the student online homework logs in order to observe engineering students’ homework learning process. A decision tree model containing all of the pre-college characteristics and online homework learning features was also developed, and it identified four key factors related to students’ risk to fail the first module exam: Correctness, Preparedness, Self-efficacy, and percentage of homework attempts after deadline (Plate). The results of the decision tree model helped identify students-at-risk at early stage of the course. Students at risk were grouped into multiple groups. The author also proposed customized interventions to help students in different at risk groups. The findings of the study helped engineering students and educators to build up a comprehensive student profile to better understand students’ academic status and learning needs in the course. Thus this study suggests ways for both the engineering educators and students to improve the learning process in a more efficient manner.