Please use this identifier to cite or link to this item:
http://hdl.handle.net/123456789/4929
DC Field | Value | Language |
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dc.contributor.author | Ismanto E. | en_US |
dc.contributor.author | Ghani, H.A. | en_US |
dc.contributor.author | Saleh N.I.B.M. | en_US |
dc.date.accessioned | 2023-10-16T02:21:04Z | - |
dc.date.available | 2023-10-16T02:21:04Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 20894872 | - |
dc.identifier.uri | http://hdl.handle.net/123456789/4929 | - |
dc.description | Scopus | en_US |
dc.description.abstract | Virtual learning environment is becoming an increasingly popular study option for students from diverse cultural and socioeconomic backgrounds around the world. Although this learning environment is quite adaptable, improving student performance is difficult due to the online-only learning method. Therefore, it is essential to investigate students' participation and performance in virtual learning in order to improve their performance. Using a publicly available Open University learning analytics dataset, this study examines a variety of machine learning-based prediction algorithms to determine the best method for predicting students' academic success, hence providing additional alternatives for enhancing their academic achievement. Support vector machine, random forest, Nave Bayes, logical regression, and decision trees are employed for the purpose of prediction using machine learning methods. It is noticed that the random forest and logistic regression approach predict student performance with the highest average accuracy values compared to the alternatives. In a number of instances, the support vector machine has been seen to outperform the other methods. | en_US |
dc.publisher | Institute of Advanced Engineering and Science | en_US |
dc.relation.ispartof | IAES International Journal of Artificial Intelligence | en_US |
dc.subject | Classification techniques | en_US |
dc.subject | Exploratory data analysis | en_US |
dc.subject | Machine learning | en_US |
dc.title | A comparative study of machine learning algorithms for virtual learning environment performance prediction | en_US |
dc.type | National | en_US |
dc.identifier.doi | 10.11591/ijai.v12.i4.pp1677-1686 | - |
dc.description.page | 1677 - 1686 | en_US |
dc.volume | 12(4) | en_US |
dc.description.type | Article | en_US |
item.grantfulltext | open | - |
item.openairetype | National | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | UNIVERSITI MALAYSIA KELANTAN | - |
Appears in Collections: | Faculty of Data Science and Computing - Journal (Scopus/WOS) |
Files in This Item:
File | Description | Size | Format | |
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22135-45787-1-PB-1.pdf | 681.99 kB | Adobe PDF | View/Open |
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