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NTIS 바로가기한국융합학회논문지 = Journal of the Korea Convergence Society, v.12 no.10, 2021년, pp.37 - 43
This study proposed an effective automatic classification technique to identify dropout patterns of university students, and based on this, an intelligent recommender system to prevent dropouts. To this end, 1) a data processing method to improve the performance of machine learning was proposed base...
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P. Perchinunno, M. Bilancia, & D. Vitale. (2021). A Statistical Analysis of Factors Affecting Higher Education Dropouts. Social Indicators Research, 156, 341-362. DOI : 10.1007/s11205-019-02249-y
E. J. Lee, Y. Song, J. H. Kim & S. H. Oh. (2020). An Exploratory Study on Determinants Predicting the Dropout Rate of 4-year Universities Using Random Forest: Focusing on the Institutional Level Factors. Journal of Educational Technology, 36(1), 191-219.
S. Zhang, L. Yao, A. Sun, & Y. Tay. (2019). Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Computing Surveys, 52(1), 1-38. DOI : 10.1145/3285029
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A. Kehagias, V. Petridis, V. G. Kaburlasos, & P. Fragkou. (2003). A Comparison of Word- and Sense-Based Text Categorization Using Several Classification Algorithms. Journal of Intelligent Information Systems, 21, 227-247. DOI : 10.1023/A:1025554732352
A. Stavrianou, C. Brun, T. Silander, & C. Roux. (2014). NLP-based feature extraction for automated tweet classification. Proceedings of the 1st International Conference on Interactions between Data Mining and Natural Language Processing, 1202, 145-146. https://aclanthology.org/2020.nlpcovid19-acl.17.pdf
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