The purpose of this study is to expand the understanding of IT student retention in Taiwan. Three objectives are proposed: (a) to identify at-risk students who are most likely to drop out; (b) to model Information System (IS) student retention; and (c) to inform intervention programs for at-risk students. The significance of this study is in serving to better inform faculties, staff and administrators of higher education institutions in turn to help strengthen the retention of IT students. In seeking to identify at-risk students most likely to drop out, two sets of secondary data were analysed. Both datasets included students’ demographic and academic performance variables, which were drawn from the student information systems of the institutions used in this study. The first dataset consisted of all-year level students who enrolled in the period 2003 to 2005. Logistic regression and Support Vector Machine (SVM), a type of machine learning technology, were used to classify at-risk students. The second dataset only included first-year students who enrolled in 2006. Logistic regression models were built to determine the significant predictors on attrition. A further two analytical steps were utilised in modelling IS student retention. Prior to building this model, the psychological factor of ‘self-efficacy’ in IT students was first examined. We introduced self-efficacy, a psychological factor that affects students’ academic outcomes, as a new factor to be incorporated into Tinto’s theory—a well-known framework in student retention research. Data gathered from a Taiwanese national survey conducted in 2005 was used. Multivariate analysis of variance (MANOVA) was used to analyse the interaction effects between academic integration and self-efficacy. The independent variables were institution type and students’ study major/discipline. Once the effect of the psychological factor on IT students was recognised, an IS student retention model was built corresponding to the factor of self-efficacy. A modified version of Tinto and Bean’s integrated model of student retention was adopted to investigate IS students in private higher education institutions in Taiwan. Tinto and Bean’s models are well respected in the area of retention research. A questionnaire survey was carried out in six private institutions. Structural equation modelling (SEM) was used to examine the parameter estimates of the measurement and structural models of the hypothesised model. In addition, face-to-face interviews were conducted to confirm the results of the SEM. Recommendations on improving student retention were obtained by using a qualitative approach. A face-to-face interview method was used to inform intervention programs for at-risk IS students. Sixteen students studying the IS discipline and four academic staff members were recruited randomly and interviewed, so that more detailed information on student retention could be gathered. The results of the research indicate that self-efficacy, commitment to goals linked to achieving the degree, and academic integration were the major contributing factors impacting on retention decisions. The limited resources dedicated to intervention strategies should focus on these students. Suggested interventions include teaching strategies aimed at improving an individual’s self-efficacy and academic performance, and career development advice aimed at enhancing their commitment to goals
