Abstract
AbstractStudent drop-out is one of the most critical issues that higher educational institutions face nowadays. The problem is significant for first-year students. These freshmen are especially at risk of failing due to the transition from different educational settings at high school. Thanks to the massive boom of Information and Communication Technologies, universities have started to collect a vast amount of study- and student-related data. Teachers can use the collected information to support students at risk of failing their studies. At the Faculty of Mechanical Engineering, Czech Technical University in Prague, the situation is no different, and first-year students are a vulnerable group similar to other institutions. The most critical part of the first year is the first exam period. One of the essential skills the student needs to develop is planning for exams. The presented research aims to explore the exam-taking patterns of first-year students. Data of 361 first-year students have been analysed and used to construct “layered” Markov chain probabilistic graphs. The graphs have revealed interesting behavioural patterns within the groups of successful and unsuccessful students.
Funder
Grantová Agentura České Republiky
Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen
Humboldt-Universität zu Berlin
Publisher
Springer Science and Business Media LLC
Subject
Computational Theory and Mathematics,Education
Reference33 articles.
1. Arnold, K.E., & Pistilli, M.D. (2012). Course signals at purdue: Using learning analytics to increase student success. Proceedings of the 2nd international conference on learning analytics and knowledge (May), 267–270. https://doi.org/10.1145/2330601.2330666
2. Bonjean, D. (2019). European credit transfer and accumulation system (ects). https://ec.europa.eu/education/resources-and-tools/european-credit-transfer-and-accumulation-system-ects_en
3. Borgwardt, K.M., & Kriegel, H.P. (2005). Shortest-path kernels on graphs. In: Fifth IEEE international conference on data mining (ICDM’05) (pp. 8–pp). IEEE
4. Bowen, W. (2015). The log-on degree; technology and universities. https://www.economist.com/united-states/2015/03/12/the-log-on-degree. Accessed 05/2015
5. Daempfle, P. A. (2003). An analysis of the high attrition rates among first year college science, math, and engineering majors. Journal of College Student Retention: Research, Theory & Practice, 5(1), 37–52.
Cited by
2 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