Time-series Insights into the Process of Passing or Failing Online University Courses using Neural-Induced Interpretable Student States
This work addresses the challenge of early intervention for at-risk students in online education, though it is incremental by combining existing data types.
The paper tackled the problem of modeling student behavioral trajectories to identify at-risk students in online university courses, and found that incorporating textual data from mentor notes improved predictive power for course failure and provided interpretable insights into engagement processes.
This paper addresses a key challenge in Educational Data Mining, namely to model student behavioral trajectories in order to provide a means for identifying students most at-risk, with the goal of providing supportive interventions. While many forms of data including clickstream data or data from sensors have been used extensively in time series models for such purposes, in this paper we explore the use of textual data, which is sometimes available in the records of students at large, online universities. We propose a time series model that constructs an evolving student state representation using both clickstream data and a signal extracted from the textual notes recorded by human mentors assigned to each student. We explore how the addition of this textual data improves both the predictive power of student states for the purpose of identifying students at risk for course failure as well as for providing interpretable insights about student course engagement processes.