Precision education with statistical learning and deep learning: a case study in Taiwan.

Autor/Urheber	Shuo-Chang Tsai; Cheng-Huan Chen; Yi-Tzone Shiao; Jin-Shuei Ciou; Trong-Neng Wu
Institution	SpringerOpen
Titel	Precision education with statistical learning and deep learning: a case study in Taiwan.
Quelle	In: doi:10.1186/s41239-020-00186-2; 2365-9440; International Journal of Educational Technology in Higher Education, Vol 17, Iss 1, Pp 1-13 (2020)(2020) PDF als Volltext kostenfreie Datei
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
DOI	10.1186/s41239-020-00186-2
Schlagwörter	Statistical learning; Machine learning; Deep learning; Dropout; Student background characteristics; Academic performance; Special aspects of education; Information technology; T58.5-58.64
Abstract	Abstract The low birth rate in Taiwan has led to a severe challenge for many universities to enroll a sufficient number of students. Consequently, a large number of students have been admitted to universities regardless of whether they have an aptitude for academic studies. Early diagnosis of students with a high dropout risk enables interventions to be provided early on, which can help these students to complete their studies, graduate, and enhance their future competitiveness in the workplace. Effective prelearning interventions are necessary, therefore students' learning backgrounds should be thoroughly examined. This study investigated how big data and artificial intelligence can be used to help universities to more precisely understand student backgrounds, according to which corresponding interventions can be provided. For this study, 3552 students from a university in Taiwan were sampled. A statistical learning method and a machine learning method based on deep neural networks were used to predict their probability of dropping out. The results revealed that student academic performance (regarding the dynamics of class ranking percentage), student loan applications, the number of absences from school, and the number of alerted subjects successfully predicted whether or not students would drop out of university with an accuracy rate of 68% when the statistical learning method was employed, and 77% for the deep learning method, in the case of giving first priority to the high sensitivity in predicting dropouts. However, when the specificity metric was preferred, then the two approaches both reached more than 80% accuracy rates. These results may enable the university to provide interventions to students for assisting course selection and enhancing their competencies based on their aptitudes, potentially reducing the dropout rate and facilitating adaptive learning, thereby achieving a win-win situation for both the university and the students. This research offers a feasible direction for using artificial intelligence applications on the basis of a university's institutional research database.
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