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A Machine Learning Classification Application to Identify Inefficient Novice Programmers

Data 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 pr...

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Main Authors: Khan I., Al-Mamari A., Al-Abdulsalam B., Al-Abdulsalam F., Al-Khansuri M., Iqbal Malik S., Ahmad A.R.
Other Authors: 58061521900
Format: Conference Paper
Published: Springer Science and Business Media Deutschland GmbH 2023
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id my.uniten.dspace-26443
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spelling my.uniten.dspace-264432023-05-29T17:10:35Z A Machine Learning Classification Application to Identify Inefficient Novice Programmers Khan I. Al-Mamari A. Al-Abdulsalam B. Al-Abdulsalam F. Al-Khansuri M. Iqbal Malik S. Ahmad A.R. 58061521900 57361613300 57361570700 57361656500 57361526700 57223048471 35589598800 Data 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; Students To 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. Final 2023-05-29T09:10:34Z 2023-05-29T09:10:34Z 2021 Conference Paper 10.1007/978-3-030-90235-3_37 2-s2.0-85120534690 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120534690&doi=10.1007%2f978-3-030-90235-3_37&partnerID=40&md5=0dbf69d9c399365b56d952a1127039a5 https://irepository.uniten.edu.my/handle/123456789/26443 13051 LNCS 423 434 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 Data 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; Students
author2 58061521900
author_facet 58061521900
Khan I.
Al-Mamari A.
Al-Abdulsalam B.
Al-Abdulsalam F.
Al-Khansuri M.
Iqbal Malik S.
Ahmad A.R.
format Conference Paper
author Khan I.
Al-Mamari A.
Al-Abdulsalam B.
Al-Abdulsalam F.
Al-Khansuri M.
Iqbal Malik S.
Ahmad A.R.
spellingShingle Khan I.
Al-Mamari A.
Al-Abdulsalam B.
Al-Abdulsalam F.
Al-Khansuri M.
Iqbal Malik S.
Ahmad A.R.
A Machine Learning Classification Application to Identify Inefficient Novice Programmers
author_sort Khan I.
title A Machine Learning Classification Application to Identify Inefficient Novice Programmers
title_short A Machine Learning Classification Application to Identify Inefficient Novice Programmers
title_full A Machine Learning Classification Application to Identify Inefficient Novice Programmers
title_fullStr A Machine Learning Classification Application to Identify Inefficient Novice Programmers
title_full_unstemmed A Machine Learning Classification Application to Identify Inefficient Novice Programmers
title_sort machine learning classification application to identify inefficient novice programmers
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806426713493602304
score 13.211869

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