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Tracking student performance in introductory programming by means of machine learning

Big data; Decision trees; Education computing; Learning algorithms; Learning systems; Machine learning; Smart city; Students; Trees (mathematics); Educational data mining; Educational institutions; Hidden patterns; Introductory programming; Introductory programming course; Student performance; Stude...

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Main Authors: Khan I., Al Sadiri A., Ahmad A.R., Jabeur N.
Other Authors: 58061521900
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-247732023-05-29T15:26:52Z Tracking student performance in introductory programming by means of machine learning Khan I. Al Sadiri A. Ahmad A.R. Jabeur N. 58061521900 57207830966 35589598800 6505727698 Big data; Decision trees; Education computing; Learning algorithms; Learning systems; Machine learning; Smart city; Students; Trees (mathematics); Educational data mining; Educational institutions; Hidden patterns; Introductory programming; Introductory programming course; Student performance; Student's performance; Weka; Data mining large amount of digital data is being generated across a wide variety of fields and Data Mining (DM) techniques are used transform it into useful information so as to identify hidden patterns. One of the key areas of the application of Education Data Mining (EDM) is the development of student performance prediction models that would predict the student's performance in educational institutions. We build a model which can notify students (in introductory programming course) about their probable outcomes at an early stage of the semester (when evaluated for 15% grades). We applied 11 Machine Learning algorithms (from 5 categories) over a data source using WEKA and concluded that Decision Tree (J48) is giving higher accuracy in terms of correctly identified instances, F-Measure rate and true positive detections. This study will help to the students to identify their probable final grades and modify their academic behavior accordingly to achieve higher grades. � 2019 IEEE. Final 2023-05-29T07:26:52Z 2023-05-29T07:26:52Z 2019 Conference Paper 10.1109/ICBDSC.2019.8645608 2-s2.0-85063188659 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063188659&doi=10.1109%2fICBDSC.2019.8645608&partnerID=40&md5=bdaddfee7e4aceae0c72beb606b95d7c https://irepository.uniten.edu.my/handle/123456789/24773 8645608 Institute of Electrical and Electronics Engineers Inc. 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 Big data; Decision trees; Education computing; Learning algorithms; Learning systems; Machine learning; Smart city; Students; Trees (mathematics); Educational data mining; Educational institutions; Hidden patterns; Introductory programming; Introductory programming course; Student performance; Student's performance; Weka; Data mining
author2 58061521900
author_facet 58061521900
Khan I.
Al Sadiri A.
Ahmad A.R.
Jabeur N.
format Conference Paper
author Khan I.
Al Sadiri A.
Ahmad A.R.
Jabeur N.
spellingShingle Khan I.
Al Sadiri A.
Ahmad A.R.
Jabeur N.
Tracking student performance in introductory programming by means of machine learning
author_sort Khan I.
title Tracking student performance in introductory programming by means of machine learning
title_short Tracking student performance in introductory programming by means of machine learning
title_full Tracking student performance in introductory programming by means of machine learning
title_fullStr Tracking student performance in introductory programming by means of machine learning
title_full_unstemmed Tracking student performance in introductory programming by means of machine learning
title_sort tracking student performance in introductory programming by means of machine learning
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806426712036081664
score 13.211869

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