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Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course

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dc.citedby5
dc.contributor.authorKhan I.en_US
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorJabeur N.en_US
dc.contributor.authorMahdi M.N.en_US
dc.contributor.authorid58061521900en_US
dc.contributor.authorid35589598800en_US
dc.contributor.authorid6505727698en_US
dc.contributor.authorid56727803900en_US
dc.date.accessioned2023-05-29T09:11:13Z
dc.date.available2023-05-29T09:11:13Z
dc.date.issued2021
dc.descriptionDecision trees; Failure analysis; Forecasting; Machine learning; Predictive analytics; F measure; Failure rate; High-accuracy; Introductory programming; Introductory programming course; Precautionary measures; Prediction model; Studentsen_US
dc.description.abstractThe new students struggle to understand the introductory programming courses, due to its intricate nature, which results in higher dropout and increased failure rates. Despite implementing productive methodologies, the instructor struggles to identify the students with distinctive levels of skills. The modern institutes are looking for technology-equipped practices to classify the students and prepare personalized consultation procedures for each class. This paper applies decision tree-based machine learning classifiers to develop a prediction model competent to forecast the outcome of the introductory programming students at an early stage of the semester. The model is then transformed into an adaptive consultation framework which generates three types of colored signals; red, yellow, and green which illustrates whether the student is performing low, average, or high respectively. This provides an opportunity for the instructor to set precautionary measures for low performing students and set complicated tasks that help the highly skilled students to improve their skills further. The experiments compare a set of decision tree-based classifiers and conclude J48 as an efficient model in classifying students in all classes with high accuracy, sensitivity, and F-measure. Even though the aim of the research is to focus on introductory programming courses, however, the framework is flexible and can be implemented in other courses. � 2021. All Rights Reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.3991/ijet.v16i17.18995
dc.identifier.epage59
dc.identifier.issue17
dc.identifier.scopus2-s2.0-85115098738
dc.identifier.spage42
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85115098738&doi=10.3991%2fijet.v16i17.18995&partnerID=40&md5=666ebdbf5a6634569f0e2930b64193dc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26497
dc.identifier.volume16
dc.publisherInternational Association of Online Engineeringen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleInternational Journal of Emerging Technologies in Learning
dc.titleMachine Learning Prediction and Recommendation Framework to Support Introductory Programming Courseen_US
dc.typeArticleen_US
dspace.entity.typePublication
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