Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course
Decision trees; Failure analysis; Forecasting; Machine learning; Predictive analytics; F measure; Failure rate; High-accuracy; Introductory programming; Introductory programming course; Precautionary measures; Prediction model; Students
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International Association of Online Engineering
2023
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my.uniten.dspace-264972023-05-29T17:11:13Z Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course Khan I. Ahmad A.R. Jabeur N. Mahdi M.N. 58061521900 35589598800 6505727698 56727803900 Decision trees; Failure analysis; Forecasting; Machine learning; Predictive analytics; F measure; Failure rate; High-accuracy; Introductory programming; Introductory programming course; Precautionary measures; Prediction model; Students The 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. Final 2023-05-29T09:11:13Z 2023-05-29T09:11:13Z 2021 Article 10.3991/ijet.v16i17.18995 2-s2.0-85115098738 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115098738&doi=10.3991%2fijet.v16i17.18995&partnerID=40&md5=666ebdbf5a6634569f0e2930b64193dc https://irepository.uniten.edu.my/handle/123456789/26497 16 17 42 59 All Open Access, Gold International Association of Online Engineering Scopus |
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| description |
Decision trees; Failure analysis; Forecasting; Machine learning; Predictive analytics; F measure; Failure rate; High-accuracy; Introductory programming; Introductory programming course; Precautionary measures; Prediction model; Students |
| author2 |
58061521900 |
| author_facet |
58061521900 Khan I. Ahmad A.R. Jabeur N. Mahdi M.N. |
| format |
Article |
| author |
Khan I. Ahmad A.R. Jabeur N. Mahdi M.N. |
| spellingShingle |
Khan I. Ahmad A.R. Jabeur N. Mahdi M.N. Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course |
| author_sort |
Khan I. |
| title |
Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course |
| title_short |
Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course |
| title_full |
Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course |
| title_fullStr |
Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course |
| title_full_unstemmed |
Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course |
| title_sort |
machine learning prediction and recommendation framework to support introductory programming course |
| publisher |
International Association of Online Engineering |
| publishDate |
2023 |
| _version_ |
1806428300787056640 |