Abstract
AbstractCollege transfer students are those who follow a different trajectory in their higher education journeys than traditional students, completing a sub-degree before pursuing a bachelor’s degree at a university. While the possibility of transferring makes higher education accessible to these students, previous studies have found that they face various challenges, from issues with course load to language challenges. This study aims to examine (1) the critical factors contributing to the success of transfer students in a language course; and (2) how transfer students perform better or worse than those who enter university directly. This study conducted learning analytics with 700 college transfer students in Hong Kong, retrieving their demographic and learning data from the learning management system and the university academic registry. The results suggest that English exam scores, current semester GPA, graduating GPA at community college and current course load are important predictors of transfer students’ success in language courses. This study also finds that transfer students have lower levels of language proficiency than direct entrants. It concludes with specific recommendations to make higher education more accessible to transfer students and suggestions on how to use learning analytics to track students with different trajectories.
Funder
Hong Kong Polytechnic University
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Education
Reference37 articles.
1. Andrade, C. (2019). Multiple testing and protection against a type 1 (false positive) error using the Bonferroni and Hochberg corrections. Indian Journal of Psychological Medicine, 41(1), 99–100.
2. Archambault, K. L. (2015). The typology and needs of American transfer students. In P. A. Sasso & P. A. DeVitis (Eds.), Today’s college students: A reader (pp. 215–224). Peter Lang.
3. Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 177–194.
4. Berland, M., Martin, T., Benton, T., Smith, C. P., & Davis, D. (2013). Using learning analytics to understand the learning pathways of novice programmers. Journal of the Learning Sciences, 22(4), 564–599. https://doi.org/10.1080/10508406.2013.836655
5. Buenaflor, S. H., Berhane, B., Fries-Britt, S., & Ogwo, A. (2022). “Everything is bigger and different”: Black engineering transfer students adjusting to the intensity and academic culture of the 4-year campus. The Urban Review, 55, 1–31.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献