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.
Highlights
As higher education has become increasingly accessible, the fostering of talent has diversified
For academic performance in the first year, all students were first divided into two groups by their dynamics of academic performance: (1) the group in which the students outperformed themselves or maintained their academic performance in the second semester when compared to their class ranking percentage from the first semester; and (2) the group in which the students performed less satisfactorily in the second semester when compared to their class ranking percentage from the first semester
The proportion of students whose final grades were lower in the second semester than in the first semester was significantly higher among dropout students than among current students
Summary
As higher education has become increasingly accessible, the fostering of talent has diversified This satisfies social and industrial demands within the workforce and is conducive to socio-economic development. To avoid the enrollment quota being reduced by the Ministry of Education (Taiwan) as a result of an overly low student enrollment rate (calculated by dividing the number of students enrolled by a university’s designated enrollment quota), which can undermine a university’s operations, universities in Taiwan have developed various strategies. Universities have all been working toward increasing their enrollment rate, and the following types of universities outperform others, with an enrollment rate exceeding 90%: national universities, which have access to larger national resources and low tuition fees; medical universities, which have specialized expertise and high employment rates; and private universities whose reputation has risen and performance has been acknowledged through their participation in the Ministry of Education’s Higher Education Sprout Project (previously known as the Teaching Excellence Project) Few universities refine their teaching strategies to enhance students’ future development potential, such as the NTU System, which established a crossuniversity course and minor selection system, and the China Asia Associated University, a university system of China Medical University and Asia University which established a cross-university minor and double major selection system. Universities have all been working toward increasing their enrollment rate, and the following types of universities outperform others, with an enrollment rate exceeding 90%: national universities, which have access to larger national resources and low tuition fees; medical universities, which have specialized expertise and high employment rates; and private universities whose reputation has risen and performance has been acknowledged through their participation in the Ministry of Education’s Higher Education Sprout Project (previously known as the Teaching Excellence Project)
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