Publication:
Selecting Machine Learning Models for Student Performance Prediction Aligned with Pedagogical Objectives

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dc.citedby0
dc.contributor.authorKhan I.en_US
dc.contributor.authorZabil M.H.M.en_US
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorJabeur N.en_US
dc.contributor.authorid58061521900en_US
dc.contributor.authorid35185866500en_US
dc.contributor.authorid35589598800en_US
dc.contributor.authorid6505727698en_US
dc.date.accessioned2024-10-14T03:19:49Z
dc.date.available2024-10-14T03:19:49Z
dc.date.issued2023
dc.description.abstractMachine learning classifiers emerge as productive tools to develop prediction models which forecast the final outcome of the students, in a course, and provide an opportunity to the instructor to take appropriate measures. A single prediction model may not be enough to achieve all the objectives of an instructor. The selection of appropriate prediction models is yet a challenging task. This paper proposes a novel framework that applies a set of machine learning classifiers over the training dataset of a course. Along with the training dataset, the instructor clarifies the prime of objective of the binary model whether it must focus over the identification of fail, pass, or both students. The framework recommends a convenient model to achieve the specific objectives of the instructor. This research concludes that models inclined to reduce misclassification of minority class are suitable if the primary objective of the institution is the correct identification of students who are struggling to achieve the minimum course requirements. Further, a model specialized solely in amplifying the correct classification of majority class instance is appropriate if the aim is to focus on the correct identification of excellent students. The empirical analysis in this research leads towards the fact that accuracy alone is not an adequate metric to assess the performance of a model and that systematic selection of evaluation metrics is required to develop constructive models. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICOCO59262.2023.10398162
dc.identifier.epage407
dc.identifier.scopus2-s2.0-85184849923
dc.identifier.spage402
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85184849923&doi=10.1109%2fICOCO59262.2023.10398162&partnerID=40&md5=3c463b57c33ac8d09e40fe084bcc47ad
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34442
dc.pagecount5
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2023 IEEE International Conference on Computing, ICOCO 2023
dc.subjectMachine Learning
dc.subjectSensitivity
dc.subjectSpecificity
dc.subjectStudents' Performance Prediction
dc.subjectClassification (of information)
dc.subjectForecasting
dc.subjectMachine learning
dc.subjectLearning classifiers
dc.subjectMachine learning models
dc.subjectMachine-learning
dc.subjectPerformance prediction
dc.subjectPrediction modelling
dc.subjectSensitivity
dc.subjectSpecificity
dc.subjectStudent performance
dc.subjectStudent' performance prediction
dc.subjectTraining dataset
dc.subjectStudents
dc.titleSelecting Machine Learning Models for Student Performance Prediction Aligned with Pedagogical Objectivesen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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