Abstract
In recent years, the phenomenon of students’ dropout has become more and more common among first year students at Ba Ria-Vung Tau College of Technology. Therefore, in this paper, it is proposed to build a model to recommend the possibility of students’ dropout. All the student data, stored in the system from 2017 to 2018, is used as the model training dataset. The student dataset in the academic year 2018–2019 is used to test the performance of the proposed model. There are 4 models has been tested, including K-means, Decision Tree, Neural Network, and Support Vector Machine. In this paper, measuring metrics including accuracy, precision, recall, f1-score are used to evaluate the performance of these models. Experimental results show that the neural network model and the support vector machine model give the best results, with the same accuracy from 95 to 96%. However, the neural network model gives results with a higher f1-score, and has the similarity of precision and recall. Therefore, the neural network was chosen as the model to recommend possibility of students’ dropout at Ba Ria-Vung Tau College of Technology.
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Phan, NH., Bui, TTT. (2023). Recommendation Model for Students Dropout at Ba Ria-Vung Tau College of Technology. In: Kumar, S., Hiranwal, S., Purohit, S.D., Prasad, M. (eds) Proceedings of International Conference on Communication and Computational Technologies . Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-3951-8_13
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DOI: https://doi.org/10.1007/978-981-19-3951-8_13
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