Time-series Insights into the Process of Passing or Failing Online University Courses using Neural-Induced Interpretable Student States

TL;DR

This study introduces a time series model combining clickstream and textual mentor notes to predict student success or failure in online courses, enhancing interpretability and predictive accuracy.

Contribution

The paper presents a novel approach integrating textual mentor notes with clickstream data in a time series model for student risk prediction and interpretability.

Findings

Textual data improves prediction accuracy of at-risk students.

The model provides interpretable insights into student engagement.

Combining data sources enhances early intervention capabilities.

Abstract

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.