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Tracking student performance in introductory programming by means of machine learning

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dc.citedby36
dc.contributor.authorKhan I.en_US
dc.contributor.authorAl Sadiri A.en_US
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorJabeur N.en_US
dc.contributor.authorid58061521900en_US
dc.contributor.authorid57207830966en_US
dc.contributor.authorid35589598800en_US
dc.contributor.authorid6505727698en_US
dc.date.accessioned2023-05-29T07:26:52Z
dc.date.available2023-05-29T07:26:52Z
dc.date.issued2019
dc.descriptionBig 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 miningen_US
dc.description.abstractlarge 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8645608
dc.identifier.doi10.1109/ICBDSC.2019.8645608
dc.identifier.scopus2-s2.0-85063188659
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85063188659&doi=10.1109%2fICBDSC.2019.8645608&partnerID=40&md5=bdaddfee7e4aceae0c72beb606b95d7c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24773
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2019 4th MEC International Conference on Big Data and Smart City, ICBDSC 2019
dc.titleTracking student performance in introductory programming by means of machine learningen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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