IR Dictionary
The IR Center completed the compilation of the IR dictionary in 2016. According to the scope of the database, it is divided into four parts: "Students", "Library", "Teachers" and "Appendix". The content includes forms, column content, data format and Encoding etc. It was formally published on March 3, 2017 by applying to the National Library of China for an ISBN (978-986-6395-84-0) with the "Asia University Institutional Research Database Dictionary". At the same time as the rolling correction database, the dictionary is also updated
The influences of Student Engagement on Academic Achievement for College Students:The Moderating Effect of Reading Engagement
Private university students in Taiwan often face the pressure of student loans and high cost of living, so they tend to work part-time while going to school. Skipping classes in order to work part-time might lead a student to decrease student engagement, which, in turn, affects academic achievement. The purpose of this study is to test whether reading engagement has a moderating effect on student engagement and academic achievement. We collected data from 4,495 Asia University students’ absenteeism rate, book borrowing records and academic performance, and then conducted empirical analysis using hierarchical regression methods. Research findings are as follows: 1. The higher the number of absenteeism, the worse the class ranking results. Therefore, we should face up to the problem of low student engagement of Taiwan’s college students and formulate strategies to improve student engagement. 2. Those who have borrowed books are 2.3 times more likely to be ranked the top half in their class than those who have not borrowed books. Therefore, strategies for improving reading engagement should be devised and integrated into teaching. 3. The method of using reading engagement to improve academic performance is more effective with high absenteeism rate students than with low absenteeism rate students. Therefore, low absenteeism rate students should be strictly required to increase their attendance rate and high absenteeism rate students should be assisted with more reading engagement.
Precision education with statistical learning and deeplearning: A case study in Taiwan
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.