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- W3189834756 abstract "In some universities, first year engineering students are not affiliated with any major or department. Major field selection creates problem to students who do not know their own preferences or want to ensure their academic achievements. Major recommendation system will be able to support their decision making and reduce number of dropout rate from improper major field selection. The approach will recommend the predicted highest cumulated GPA major field by using historical grades from first year courses. This research focused on classification techniques to discover a proper major for engineer students. A total of 1,501 student records has been collected from year 2014 to 2016. Three classification models are developed consisted of decision tree, k-nearest neighbors, and artificial neural network. Then, linear regression is used to predict cumulative GPA in top 5 recommendation major. The results show that k-nearest neighbors model provides the best prediction with 81% accuracy." @default.
- W3189834756 created "2021-08-16" @default.
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- W3189834756 date "2021-05-26" @default.
- W3189834756 modified "2023-09-23" @default.
- W3189834756 title "A Major Recommendation System in Educational Mining" @default.
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- W3189834756 doi "https://doi.org/10.1109/cymaen50288.2021.9497279" @default.
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