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  • ime-series Insights into the Process of Passing or Failing Online University Courses using Neural-Induced Interpretable Student States
Title: ime-series Insights into the Process of Passing or Failing Online University Courses using Neural-Induced Interpretable Student States
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 click stream 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 click stream 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.  more » « less
Award ID(s):
1822831
PAR ID:
10100851
Author(s) / Creator(s):
Date Published:
Journal Name:
Proceedings of the 12th International Conference on Educational Data Mining
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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