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

The 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 inst...

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Main Authors: Khan I., Ahmad A.R., Jabeur N., Mahdi M.N.
Other Authors: 58061521900
Format: Conference Paper
Published: Springer Science and Business Media Deutschland GmbH 2023
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spelling my.uniten.dspace-272822023-05-29T17:42:06Z A Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Procedures Khan I. Ahmad A.R. Jabeur N. Mahdi M.N. 58061521900 35589598800 6505727698 56727803900 The 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. Final 2023-05-29T09:42:05Z 2023-05-29T09:42:05Z 2022 Conference Paper 10.1007/978-3-030-85990-9_23 2-s2.0-85121843740 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121843740&doi=10.1007%2f978-3-030-85990-9_23&partnerID=40&md5=8ca53349a02518a70d628a9056687e7c https://irepository.uniten.edu.my/handle/123456789/27282 322 269 280 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description The 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.
author2 58061521900
author_facet 58061521900
Khan I.
Ahmad A.R.
Jabeur N.
Mahdi M.N.
format Conference Paper
author Khan I.
Ahmad A.R.
Jabeur N.
Mahdi M.N.
spellingShingle Khan I.
Ahmad A.R.
Jabeur N.
Mahdi M.N.
A Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Procedures
author_sort Khan I.
title A Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Procedures
title_short A Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Procedures
title_full A Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Procedures
title_fullStr A Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Procedures
title_full_unstemmed A Systematic Approach to Transform Machine Learning Students� Performance Prediction Model into Preventive Procedures
title_sort systematic approach to transform machine learning students� performance prediction model into preventive procedures
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806423429702746112
score 13.211869

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