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

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.

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

“…(1) Education is progressively moving from a one-size-fits-all approach to precision education or personalized learning FIGURE 1 | Contemporary developments and future trends at the intersections between research, policy, and industry driven by big data and AI advances in education. (Lu et al, 2018;Tsai et al, 2020). The one-size-fits-all approach was designed for average students, whereas precision education takes into consideration the individual differences of learners in their learning environments, along with their learning strategies.…”

Predicting at-risk university students based on their e-book reading behaviours by using machine learning classifiers

“…In recent years considerable interest has emerged in the domain of education research regarding the application of artificial intelligence Hwang et al, 2020;. One development addresses how predicting students' performance has become an essential research topic in different academic disciplines (e.g., Lu et al, 2018;Tsai et al, 2020), and the goal of being able to predict levels for students learning performance, score, ability or mark from specific information, aspects or behaviour of those students (Romero & Ventura, 2013). For example, how instructors track behaviour patterns and find student diversity to enable them to apply appropriate educational approaches (Xenos, 2004), or how instructors should try to identify high-risk students in a cohort, identify possible dropouts, or failed course students, and prevent this from happening (Heuer & Breiter, 2018;Wakelam et al, 2019;Wu et al, 2020).…”

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

“…Here, the students are guided for their own learning, can follow the pace they want, and make their own decisions about what to learn. Some state-of-the-art research contributions in the context of DL techniques for the education industry can be found here [89][90][91][92][93][94]. A comparison of some prominent studies is presented in Table 3.…”