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. However, the excessive accessibility of higher education in Taiwan has been observed to cause numerous adverse effects. For example, universities have struggled to achieve sufficiently high enrollment. 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. For example, a few private universities set their tuition at the same level as those of national universities; some medical universities waive tuition and fees for students with academic excellence in some departments; and some universities offer scholarships to elite students. Moreover, few universities refine their teaching strategies to enhance students’ future development potential, such as the NTU System, which established a cross-university course and minor selection system,Footnote 1 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.Footnote 2 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).
However, students meet two immediate challenges after enrollment: whether they can adapt to university life and whether their department caters to their interest. Before the Ministry of Education’s Teaching Excellence Project began, students were fully responsible for their learning performance. Since the project’s inception in 2004, the academic performance alert system has been uniformly adopted by universities. Although universities employed different approaches to system implementation and designed dissimilar contents, universities and teachers are common in that they have taken responsibility for students’ learning performance. After the Teaching Excellence Project began (see Fig. 1, which will also be described later), an early alert system, which is used to identify students with unfavorable academic performance, has been adopted by most higher education institutions. However, not every university achieved expected outcomes. A comprehensive alert system can help educators and relevant personnel to identify students with a high risk of low academic performance and thereby implement appropriate measures to avoid these students experiencing involuntary suspension of their studies and dropping out altogether. Specifically, this system should enable educators to provide remedial teaching resources and interventions to such students to help them keep up with other students and graduate punctually, a solution which can improve social mobility (McKenzie, 2018).
Although the alert system has achieved certain success in universities, various factors have resulted in growing numbers of students facing suspension or dropping out; these students fail to complete their studies and choose to suspend their studies or to drop out for financial/personal reasons or academic performance. The suspension and dropout rate increased from 4% in 1998 to 15% in 2017 school year (viz., academic year, which in Taiwan refers to a one-year period from August 1 of that year to July 31 of the next year). Experts have indicated that, in addition to the aforementioned factors, this rising rate might have been partially caused by the universalization of higher education and admission via the Star Plan (in which students graduating from high schools on the plan list may apply) enacted from the 2009 school year; students may be unsure about which major to choose, and consequently, a proportion of students find out that the department they have chosen does not accommodate their interest only after they embark upon their studies. Decisions regarding student admission into universities through the Star Plan tend to favor college choice, but not major selection; in addition, their test performance on the college entrance exam receives significantly less consideration. It can be seen from the university database of the Ministry of Education (via https://udb.moe.edu.tw) that, the total number of students dropping out increased from 84,719 in the 2012 school year to 91,556 in the 2017 school year.
The ease with which students may transfer between universities and the uniformity of tuition and fees across universities in Taiwan have resulted in universities’ focus on enrollment rate and negligence of student retention and graduation rates. Although in-coming transfer students can compensate for the quota left by out-going ones, transfer students’ quality may be lower than that of non-transfer students. Our university has adhered to the teaching philosophy of “Give up on no one.” Particularly in the face of increasingly disparate learning performance among students, this university has formulated policies for early diagnosis and prognosis of its students’ learning performance and has established differentiated learning paths and instructional intervention measures that foster student competitiveness in the workplace. Adaptive teaching is no longer merely an abstract ideal for our university; it has been enacted as a school policy in the university (Wu & Tsai, 2019). To achieve the goals stipulated in the 2018 Higher Education Sprout Project, our university has been working on implementing the first phase of “precision education” (as shown in Fig. 1), which involves predicting the probability of new students failing throughout their learning in this university, and implementing instructional interventions and stratified education according to the prediction results. This may reduce the probability of new students suspending their studies or dropping out. To realize such innovative thoughts and methods, a theoretical foundation should be established for further relevant discussion and investigation in the academic community.
The precision education initiative (Hart, 2016) is inspired by precision medicine, as proposed by former US president Barack Obama in 2015 (Collins & Varmus, 2015; The White House, 2015) and has flourished in the medical system as of late despite its relatively recent emergence. For instance, Google employed the lung cancer scan results produced by the National Cancer Institute and Northwestern University to train a neural network for malignant tumor prediction. The network’s prediction capacity is comparable to or even higher than the diagnostic capacity of a trained radiologist; an early diagnosis can increase patient survival rate by 40% (Ardila et al., 2019). Teams from the Computer Science and Artificial Intelligence Laboratory of the Massachusetts Institute of Technology (MIT CSAIL) and the Massachusetts General Hospital established a deep learning model that could predict a patient’s probability of developing breast cancer in 5 yrs on the basis of their breast X-ray images (Yala, Lehman, Schuster, Portnoi, & Barzila, 2019). More recently, National Yang-Ming University, National Chiao Tung University, and the Academia Sinica of Taiwan have established the Digital Medicine Alliance,Footnote 3 which focuses on the applications of the internet of things and big data in medicine. Currently, their research addresses the fourth most common cause of death in Taiwan, stroke, with the aim of providing precision prevention and treatments; the researchers employed AI to quickly distinguish between, for example, potential hemorrhagic stroke or ischemic stroke, and thus facilitate the provisions of treatment within the golden hour.
AI applications have been increasingly prevalent in various domains and applications. For example, IBM has developed a system that can predict the time when an employee intends to resign on the Watson supercomputer, which achieved 95% accuracy. This system saved IBM $300 million on employee retention each year (Rosenbaum, 2019); incorporating data from various sources, IBM identified potential employees who might resign in the near future, enabling the company to negotiate with the employees regarding pay raises, compensation for education expenses, and financial compensation. Additionally, since 2019, Amazon has started using AI to determine the time off task of warehouse workers and to automatically pick people to fire when necessary (Bort, 2019). With a different goal but a similar approach, this study was not concerned with the discharging of students but with discovering and solving their problems at an early stage (see, for example, Lu et al., 2018, in a blended course). To estimate the probability of new university students failing throughout their learning process, AI applications can be employed as such (Wu, Chen, & Tsai, 2018). There have been several attempts (e.g., Abu Zohair, 2019; Hew, Hu, Qiao, & Tang, 2020; Pérez, Castellanos, & Correal, 2018) to predict student performance or dropout using algorithms in higher education research in order to help at-risk students by assuring their retention. For instance, to predict students’ performance in a university course, Abu Zohair (2019) used clustering algorithms and a small dataset for training and model construction, establishing a reliable and accurate prediction model with a prediction accuracy of approximately 70%. Hew et al. (2020) adopted the supervised machine learning algorithm and hierarchical linear modelling to analyze the features of massive open online courses (MOOCs) and students’ perceptions of MOOCs; they found that several course features such as instructor, content, assessment, and schedule significantly predict student satisfaction. In a recent systematic review (Zawacki-Richter, Marín, Bond, & Gouverneur, 2019) of research on AI applications in higher education, the paper found that studies pertaining to dropout and retention intended to develop early warning systems to detect at-risk students in their first year. This aim is also a focus of our study, as attrition is more likely to happen within the first year. Although Kintu, Zhu, and Kagambe’s (2017) study did not use AI algorithms, they investigated the effectiveness of a blended learning environment supported by a learning management system, Moodle, through exploring the relationships among student characteristics/backgrounds and academic performance. The present study also incorporates student backgrounds and learning data to predict the possibility of new university students dropping out in subsequent years of their university studies; that is, this research attempted to employ these data to identify students who may drop out as a result of learning underperformance, thereby facilitating the implementation of measures involving remedial teaching or learning assistance.
The next topic to be considered was whether to adopt machine learning or statistical learning in concern for suspension and dropping out of school in higher education. The two methods exhibit various similarities; for example, both make predictions based on models established using extensive data analysis. Therefore, many people cannot distinguish between them and consider machine learning to be enhanced statistical learning. Recently Stewart (2019) clarified the differences between the two in nature by discussing the difference between statistics and machine learning. The greatest distinction between statistical and machine learning lies in their purposes. A statistical learning model is designed to infer the relationships between variables, whereas a machine learning model aims to maximize the accuracy of prediction. That is, machine learning focuses on prediction results, and statistical learning centers on causal inferences. Statistical neural networks have grown considerably in terms of high complexity problems and algorithmic efficiency in the recent decade; deep learning provides the most advanced and accurate performance in some challenging real-world machine learning tasks (Fiser, Berkes, Orbán, & Lengyel, 2010; Hinton, 2007). Hinton, Osindero, and Teh (2006) proposed deep belief nets, an unsupervised, greedy layer-by-layer pretraining scheme that targeted the vanishing gradient effect so as to make machine learning more accurate and outstanding. A machine learning method based on deep neural networks was thus employed along with statistical learning to make predictions on the basis of one single institutional database, with an aim of achieving feasible and interpretable prediction results, thereby improving student learning.