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A Machine Learning Classification Application to Identify Inefficient Novice Programmers

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dc.citedby2
dc.contributor.authorKhan I.en_US
dc.contributor.authorAl-Mamari A.en_US
dc.contributor.authorAl-Abdulsalam B.en_US
dc.contributor.authorAl-Abdulsalam F.en_US
dc.contributor.authorAl-Khansuri M.en_US
dc.contributor.authorIqbal Malik S.en_US
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorid58061521900en_US
dc.contributor.authorid57361613300en_US
dc.contributor.authorid57361570700en_US
dc.contributor.authorid57361656500en_US
dc.contributor.authorid57361526700en_US
dc.contributor.authorid57223048471en_US
dc.contributor.authorid35589598800en_US
dc.date.accessioned2023-05-29T09:10:34Z
dc.date.available2023-05-29T09:10:34Z
dc.date.issued2021
dc.descriptionData mining; Graphical user interfaces; Learning algorithms; Machine learning; Nearest neighbor search; Academic performance; Application layers; Computer science students; Educational data mining; Educational Institutes; K-near neighbor; Machine learning classification; Nearest-neighbour; Novice programmer; Productive tools; Studentsen_US
dc.description.abstractTo preserve their reputation and prestige, the educational institutes are required to provide evidences of their students� academic performance to the governmental bureaus and accreditation agencies. As a consequence, the monitoring individual student academic performance is emerging as a vital task for the educational institutes. The indispensability of this prediction amplifies when it comes to programming language course; which emerges as backbone for Computer Science students. Machine Learning classifiers are considered as productive tools to develop models which can identify the students with inefficient academic performance. The early identification of inefficient students will provide an opportunity to instructor to take appropriate precautionary measures. This paper proposes a prediction model with an added application layer with graphical user interface. The experimental part of paper compares the performance of several machine learning algorithms and comes up with k-NN as appropriate classifier in the addressed context. Further, the application layer of the proposed architecture facilitates instructor with a Graphical User Interface to execute a wide range of operations. � 2021, Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-030-90235-3_37
dc.identifier.epage434
dc.identifier.scopus2-s2.0-85120534690
dc.identifier.spage423
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85120534690&doi=10.1007%2f978-3-030-90235-3_37&partnerID=40&md5=0dbf69d9c399365b56d952a1127039a5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26443
dc.identifier.volume13051 LNCS
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.titleA Machine Learning Classification Application to Identify Inefficient Novice Programmersen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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