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dc.contributor.authorRohani, Narjes
dc.contributor.authorGal, Kobi
dc.contributor.authorGallagher, Michael
dc.contributor.authorManataki, Areti
dc.contributor.editorFeng, Mingyu
dc.contributor.editorKäser, Tanja
dc.contributor.editorTalukdar, Partha
dc.date.accessioned2023-08-01T16:30:08Z
dc.date.available2023-08-01T16:30:08Z
dc.date.issued2023-07-05
dc.identifier286427476
dc.identifier46aa2a72-5af4-4b75-b099-39ae19211e55
dc.identifier.citationRohani , N , Gal , K , Gallagher , M & Manataki , A 2023 , Early prediction of student performance in a health data science MOOC . in M Feng , T Käser & P Talukdar (eds) , Proceedings of the 16th international conference on educational data mining (EDM 2023) . International Educational Data Mining Society , Online , pp. 325–333 , International Conference on Educational Data Mining (EDM 2023) , Bengaluru , India , 11/07/23 . https://doi.org/10.5281/zenodo.8115721en
dc.identifier.citationconferenceen
dc.identifier.isbn9781733673648
dc.identifier.otherORCID: /0000-0003-3698-8535/work/139965077
dc.identifier.urihttps://hdl.handle.net/10023/28076
dc.descriptionFunding: This work was supported by the Medical Research Council [grant number MR/N013166/1].en
dc.description.abstractMassive 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.
dc.format.extent9
dc.format.extent7446844
dc.language.isoeng
dc.publisherInternational Educational Data Mining Society
dc.relation.ispartofProceedings of the 16th international conference on educational data mining (EDM 2023)en
dc.subjectStudent performanceen
dc.subjectNeural networksen
dc.subjectMOOCsen
dc.subjectExplainabilityen
dc.subjectHealth data scienceen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectR Medicineen
dc.subjectNSen
dc.subjectMCCen
dc.subject.lccQA75en
dc.subject.lccRen
dc.titleEarly prediction of student performance in a health data science MOOCen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.identifier.doi10.5281/zenodo.8115721
dc.identifier.urlhttps://educationaldatamining.org/edm2023/proceedings/index.htmlen


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