Factors Affecting Attrition among First Year Computer Science Students: the Case of University of Latvia
DOI:
https://doi.org/10.17770/etr2015vol3.174Keywords:
Attrition rate, computer science education, data processing, data analysisAbstract
The purpose of our study was to identify reasons for high dropout of students enrolled in the first year of the computer science study program to make it possible to determine students, who are potentially in risk. Several factors that could affect attrition, as it was originally assumed, were studied: high school grades (admission score), compensative course in high school mathematics, intermediate grades for core courses, prior knowledge of programming. However, the results of our study indicate that none of the studied factors is determinant to identify those students, who are going to abandon their studies, with great precision. The majority of the studied students drop out in the 1st semester of the 1st year, and the dropout consists mostly of those, who do not really begin studies. Therefore, one of the main conclusions is such that the planned activities of informing about the contents of the program should be carried out, and the perspective students should be offered a possibility to evaluate their potential to study computer science before choosing a study program.
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References
OECD, “How many students drop out of tertiary education?”, in Highlights from Education at a Glance 2010, OECD Publishing, 2010.
Overview about higher education in Latvia, Available: http://izm.izm.gov.lv/registri-statistika/statistika-augstaka/2013.html , [Accessed: Mar.17., 2015].
Overview about higher education in Latvia, Available: http://izm.izm.gov.lv/upload_file/Registri_statistika/Pielikums_2009.xls , [Accessed: Mar.17., 2015]
OECD, “Education at a Glance 2004: OECD Indicators”, OECD Publishing, 2004.
V. Tinto,. “Completing college: Rethinking institutional action”, University of Chicago Press, 2012.
F. Belloc, A. Maruotti, and L. Petrella, “University drop-out: an Italian experience”, Higher Education, vol. 60(2), pp. 127-138, 2010.
K. McKenzie and R. Schweitzer, “Who Succeeds at University? Factors predicting academic performance in first year Australian university students”, Higher Education Research & Development, vol. 20(1), pp. 21-33, 2001.
L. Grebennikov and M. Shah, "Investigating attrition trends in order to improve student retention", Quality Assurance in Education, Vol. 20 (3), pp. 223 – 236, 2012.
A. L. Reynolds and M. J. Weigand, “The relationships among academic attitudes, psychological attitudes, and the first-semester academic achievement of first-year college students”, Journal of Student Affairs Research and Practice, vol.47 (2), pp. 175–195, 2010.
F. Araque, C. Roldán, and A. Salguero, “Factors influencing university drop out rates”, Computers and Education, vol. 53(3), pp. 563-574, 2009.
L. Paura and I. Arhipova, “Cause Analysis of Students’ Dropout Rate in Higher Education Study Program”, Procedia-Social and Behavioral Sciences, vol. 109, pp. 1282-1286, 2014.
N. Gury, “Dropping out of higher education in France: a micro-economic approach using survival analysis”, Education Economics, vol.19(1), pp. 51-64, 2011.
C. Marquez-Vera, C. Romero, and S. Ventura, “Predicting School Failure Using Data Mining”, In Educational Data Mining, pp. 271-276, 2011.
“School of Young Programmers” http://www.df.lu.lv/nacstudet/jauno-datoriku-skola/, [Accessed: Mar.17., 2015].
