Abstract: Blended courses, which mix in-person instruction with online platforms are increasingly common in secondary education. These platforms record a rich amount of data on students’ study habits and social interactions. Prior research has shown that these metrics are correlated with students’ performance in face to face classes. However, predictive models for blended courses are still limited and have not yet succeeded at early prediction or cross-class predictions even for repeated offerings of the same course. In this work, we use data from two offerings of two different undergraduate courses to train and evaluate predictive models of student performance based upon persistent student characteristics including study habits and social interactions. We analyze the performance of these models on the same offering, on different offerings of the same course, and across courses to see how well they generalize. We also evaluate the models on different segments of the courses to determine how early reliable predictions can be made. In the end, we study sequences of student transitions between online tools in blended courses and identify which habits make the most difference between the higher and lower-performing groups. This work tells us in part how much data is required to make robust predictions and how cross-class data may be used, or not, to boost model performance. The results of this study will help us better understand how similar the study habits, social activities, and the teamwork styles are across semesters for students in each performance category. These trained models also provide an avenue to improve our existing support platforms to better support struggling students early in the semester with the goal of providing timely intervention.

Short Bio: Niki Gitinabard is a Ph.D. Student at North Carolina State University, working with Dr. Collin F. Lynch. Her research is focused on modeling different student activities on online platforms associated with classes. Currently, she is working on extracting useful behavioral patterns from students' online activities and training prediction models on their performance through social network analysis, sequence mining, and the analysis of their teamwork habits.