Early Detection At-Risk Students using Machine Learning

© 2020 IEEE. Machine Learning is one of the most popular technologies using in many industries, especially to analyze the data and find key insight or new knowledge. In education industry, many studies have applied machine learning techniques for various purposes. One important area is to early dete...

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Main Authors: Siripen Pongpaichet, Sawarin Jankapor, Sarun Janchai, Todsaporn Tongsanit
Other Authors: Mahidol University
Format: Conference or Workshop Item
Published: 2021
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/60914
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spelling th-mahidol.609142021-02-03T13:23:16Z Early Detection At-Risk Students using Machine Learning Siripen Pongpaichet Sawarin Jankapor Sarun Janchai Todsaporn Tongsanit Mahidol University Computer Science © 2020 IEEE. Machine Learning is one of the most popular technologies using in many industries, especially to analyze the data and find key insight or new knowledge. In education industry, many studies have applied machine learning techniques for various purposes. One important area is to early detect at-risk students by using data from various sources such as log data from learning management systems (LMSs), class attendances, and actual score from both formative and summative assessments. We present a comparative study aiming to find the most important features and the best classification algorithms to classify at-risk students based on they behaviors. The data are collected from Moodle system [1], printing services system, and students grad system at one of the faculty in the university. The experiment results are evaluated in terms of overall accuracy, precision, and recall. The random forest with oversampling on minority class shows the best result. The performances of the models is better when we have more data in each week of the semester. During week 5, the model can detect about 74 percent of at-risk students. 2021-02-03T06:23:16Z 2021-02-03T06:23:16Z 2020-10-21 Conference Paper International Conference on ICT Convergence. Vol.2020-October, (2020), 283-287 10.1109/ICTC49870.2020.9289185 21621241 21621233 2-s2.0-85098943040 https://repository.li.mahidol.ac.th/handle/123456789/60914 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85098943040&origin=inward
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Computer Science
spellingShingle Computer Science
Siripen Pongpaichet
Sawarin Jankapor
Sarun Janchai
Todsaporn Tongsanit
Early Detection At-Risk Students using Machine Learning
description © 2020 IEEE. Machine Learning is one of the most popular technologies using in many industries, especially to analyze the data and find key insight or new knowledge. In education industry, many studies have applied machine learning techniques for various purposes. One important area is to early detect at-risk students by using data from various sources such as log data from learning management systems (LMSs), class attendances, and actual score from both formative and summative assessments. We present a comparative study aiming to find the most important features and the best classification algorithms to classify at-risk students based on they behaviors. The data are collected from Moodle system [1], printing services system, and students grad system at one of the faculty in the university. The experiment results are evaluated in terms of overall accuracy, precision, and recall. The random forest with oversampling on minority class shows the best result. The performances of the models is better when we have more data in each week of the semester. During week 5, the model can detect about 74 percent of at-risk students.
author2 Mahidol University
author_facet Mahidol University
Siripen Pongpaichet
Sawarin Jankapor
Sarun Janchai
Todsaporn Tongsanit
format Conference or Workshop Item
author Siripen Pongpaichet
Sawarin Jankapor
Sarun Janchai
Todsaporn Tongsanit
author_sort Siripen Pongpaichet
title Early Detection At-Risk Students using Machine Learning
title_short Early Detection At-Risk Students using Machine Learning
title_full Early Detection At-Risk Students using Machine Learning
title_fullStr Early Detection At-Risk Students using Machine Learning
title_full_unstemmed Early Detection At-Risk Students using Machine Learning
title_sort early detection at-risk students using machine learning
publishDate 2021
url https://repository.li.mahidol.ac.th/handle/123456789/60914
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