The Road Not Taken: Preempting Dropout in MOOCs

Lele Sha, Ed Fincham, Lixiang Yan, Tongguang Li, Dragan Gašević, Kobi Gal, Guanliang Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Massive Open Online Courses (MOOCs) are often plagued by a low level of student engagement and retention, with many students dropping out before completing the course. In an effort to improve student retention, educational researchers are increasingly turning to the latest Machine Learning (ML) models to predict student learning outcomes, based on which instructors can provide timely support to at-risk students as the progression of a course. Though achieving a high prediction accuracy, these models are often “black-box” models, making it difficult to gain instructional insights from their results, and accordingly, designing meaningful and actionable interventions remains to be challenging in the context of MOOCs. To tackle this problem, we present an innovative approach based on Hidden Markov Model (HMM). We devoted our efforts to model students’ temporal interaction patterns in MOOCs in a transparent and interpretable manner, with the aim of empowering instructors to gain insights about actionable interventions in students’ next-step learning activities. Through extensive evaluation on two large-scale MOOC datasets, we demonstrated that, by gaining a temporally grounded understanding of students’ learning processes using HMM, both the students’ current engagement state and potential future state transitions could be learned, and based on which, an actionable next-step intervention tailored to the student current engagement state could be formulated to recommend to students. These findings have strong implications for real-world adoption of HMM for promoting student engagement and preempting dropouts.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education - 24th International Conference, AIED 2023, Proceedings
EditorsNing Wang, Genaro Rebolledo-Mendez, Noboru Matsuda, Olga C. Santos, Vania Dimitrova
PublisherSpringer Science and Business Media Deutschland GmbH
Pages164-175
Number of pages12
ISBN (Print)9783031362712
DOIs
StatePublished - 1 Jan 2023
Event24th International Conference on Artificial Intelligence in Education, AIED 2023 - Tokyo, Japan
Duration: 3 Jul 20237 Jul 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13916 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Artificial Intelligence in Education, AIED 2023
Country/TerritoryJapan
CityTokyo
Period3/07/237/07/23

Keywords

  • Hidden Markov Models
  • MOOCs Dropout
  • Student Engagement

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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