Publication:
A Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Procedures

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dc.contributor.authorKhan I.en_US
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorJabeur N.en_US
dc.contributor.authorMahdi M.N.en_US
dc.contributor.authorid58061521900en_US
dc.contributor.authorid35589598800en_US
dc.contributor.authorid6505727698en_US
dc.contributor.authorid56727803900en_US
dc.date.accessioned2023-05-29T09:42:05Z
dc.date.available2023-05-29T09:42:05Z
dc.date.issued2022
dc.description.abstractThe modern day educational institutes are craving novel procedures to utilize the students� data to amplify their prestige and improve the education quality. One of the major problems an instructor/institute experiences is the thorough monitoring of students� academic progress, in a course, and instigate preventive procedures to offer additional support to the students with unsatisfactory academic progress. Educational Data Mining tools, specifically Machine learning classifiers, appear supportive to develop prediction models which forecast students� final outcome in a course. This research evaluates the effectiveness of machine learning classifiers to monitor students� academic progress and informs the instructor about the students at the risk of producing unsatisfactory final result in a course. The dataset is pre-processed with Pearson correlation feature selection algorithm to discover the features which influence the students� academic performance. A set of machine learning models are developed and compared through accuracy, sensitivity, specificity, F-measure and Mathew Correlation Coefficient, to choose the finest model. J48 decision tree prevails other models by achieving accuracy and F-measure of nearly 0.90. Simple logistic appeared the least effective model while an altered version of k-nearest neighbor achieved highest sensitivity but remain ineffective due to lower accuracy and F-Measure. The ideal model is further transformed into easily explicable format and then interpreted into a set of supportive measures to carefully monitor students� performance from the very start of the course and a set of preventive measures to offer additional attention to the struggling students. � 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-030-85990-9_23
dc.identifier.epage280
dc.identifier.scopus2-s2.0-85121843740
dc.identifier.spage269
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85121843740&doi=10.1007%2f978-3-030-85990-9_23&partnerID=40&md5=8ca53349a02518a70d628a9056687e7c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27282
dc.identifier.volume322
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Networks and Systems
dc.titleA Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Proceduresen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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