University of Tartu (ESTONIA)
About this paper:
Appears in: INTED2014 Proceedings
Publication year: 2014
Pages: 2521-2529
ISBN: 978-84-616-8412-0
ISSN: 2340-1079
Conference name: 8th International Technology, Education and Development Conference
Dates: 10-12 March, 2014
Location: Valencia, Spain
In higher education, particularly in the fields of engineering and science, large dropout rates and slow progress through the curriculum are world-wide problems. Possible causes for the high attrition rate range from unpreparedness in analytical thinking or poor teaching practices (Beaubou & Mason, 2002) to a lack of motivation on the part of students (French, Immekus, & Oakes, 2005). In Estonia most dropouts usually occur during the first year of studies. The reasons for this have not been studied in detail.

In an attempt to get a better overview of how strong roles these different factors have in the local context, a study was carried out on a cohort of Computer Science majors who started their studies in fall 2012 (156 students). We gathered three separate sources of information. The first was their academic record, which reflected admittance scores (calculated based on the results of math and language exams for high school graduation) as well as marks in different university courses and whether the student had been exmatriculated. The second was their answers to two questionnaires – one filled out during their first lecture at the beginning of their studies, and another at the end of the first semester. The third and most unique source of information was their study diary, which students filled out online on a daily basis and throughout the semester. The study diary reflected how much time students reported spending on studying, and in part the methods they used while studying.
To date this cohort has completed two full semesters, and this study shows how their dropout can be predicted by the amount of credits they collect in the first year.

Our analysis shows that the results of the first semester courses can be predicted to a high degree (R=0.34) with a linear model that takes into account both the time students spend studying and their admittance scores. Each factor taken separately is considerably weaker than when taken together, thus offering evidence of an important relationship. This is in line with previous research (e.g. Nonis & Hudson, 2006), but the current study identified a new interaction that has stronger effect on the study results.

The study diary data offers other interesting insights. It was found that not all courses are created equal in terms of studying time. Two out of six courses take over 67% of the students’ time while nominally awarding only 40% of the credit points. Another empirical observation that is confirmed by the data is that students spend considerably more time on a course than usual if a midterm examination is approaching – but interestingly the size of the difference varies widely between different courses.
The outcomes of this study can be applied for identifying students likely to drop out and for providing support to manage their personal learning needs. Also this study can provide new directions for future research on predicting students’ progress in Computer Science.

[1] Beaubouef, T. & Mason, J. (2002). Why the high attrition rate for computer science students: Some thoughts and observations. ACM SIGCSE Inroads Bulletin, 37, 103–106.
[2] French, B. F., Immekus, J. C., & Oakes, W. C. (2005). An examination of indicators of engineering students’ success and persistence. Journal of Engineering Education, 94, 419–425.
[3] Nonis, S. A. & Hudson, G. I. (2006). Academic Performance of College Students: Influence of Time Spent Studying and Working. Journal of Education for Business, 81, 151–159.
Computer Science education, dropout rate, study time, learning skills.