Early prediction of student performance in a health data science MOOC

Narjes Rohani*, Kobi Gal, Michael Gallagher, Areti Manataki

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Massive Open Online Courses (MOOCs) make high-quality learning accessible to students from all over the world. On the other hand, they are known to exhibit low student performance and high dropout rates. Early prediction of student performance in MOOCs can help teachers intervene in time in order to improve learners' future performance. This is particularly important in healthcare courses, given the acute shortages of healthcare staff and the urgent need to train data-literate experts in the healthcare field. In this paper, we analysed a health data science MOOC taken by over 3,000 students. We developed a novel three-step pipeline to predict student performance in the early stages of the course. In the first step, we inferred the transitions between students' low-level actions from their clickstream interactions. In the second step, the transitions were fed into Artificial Neural Network (ANN) that predicted student performance. In the final step, we used two explanation methods to interpret the ANN result. Using this approach, we were able to predict learners' final performance in the course with an AUC ranging from 83% to 91%. We found that students who interacted predominately with lab, project, and discussion materials outperformed students who interacted predominately with lectures and quizzes. We used the DiCE counterfactual method to automatically suggest simple changes to the learning behaviour of low- and moderate-performance students in the course that could potentially improve their performance. Our method can be used by instructors to help identify and support struggling students during the course.
Original languageEnglish
Title of host publicationProceedings of the 16th international conference on educational data mining (EDM 2023)
EditorsMingyu Feng, Tanja Käser, Partha Talukdar
Place of PublicationOnline
PublisherInternational Educational Data Mining Society
Pages325–333
Number of pages9
ISBN (Electronic)9781733673648
DOIs
Publication statusPublished - 5 Jul 2023
EventInternational Conference on Educational Data Mining (EDM 2023) - Bengaluru, India
Duration: 11 Jul 202314 Jul 2023
Conference number: 16
https://educationaldatamining.org/edm2023/about-the-conference/

Conference

ConferenceInternational Conference on Educational Data Mining (EDM 2023)
Abbreviated titleEDM 2023
Country/TerritoryIndia
CityBengaluru
Period11/07/2314/07/23
Internet address

Keywords

  • Student performance
  • Neural networks
  • MOOCs
  • Explainability
  • Health data science

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