Abstract
A crucial problem that we are currently facing at the Faculty of Computing of the University of Latvia is that during the first study semester on average 30% of the first-year students drop out, whereas after the first year of studies the number of dropouts increases up to nearly 50%. Thus, our overall goal is to determine in advance applicants that most likely will not finish the first study year successfully. A hypothesis formulated in another research study was that programming aptitude could be predicted based on the results of two personality self-report questionnaires − Systemizing Quotient (SQ) and Empathy Quotient (EQ) − taken by students. The difference between the SQ and EQ scores had a strong correlation with grades received for programming test. We reproduced the circumstances of mentioned empirical study with our first-year students using similar tests to calculate SQ and EQ, and semester grades in introductory programming course as a quantitative measure to evaluate programming ability. In this paper, we elaborate on the empirical setting, measures, and estimation methods of our study, which produced the results that made us call the stated hypothesis into question and disprove it.
Keywords
- Programming aptitude
- Systemizing quotient
- Empathy quotient
- Correlation
- Dropout rate
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Borzovs, J. et al. (2017). Can SQ and EQ Values and Their Difference Indicate Programming Aptitude to Reduce Dropout Rate?. In: , et al. New Trends in Databases and Information Systems. ADBIS 2017. Communications in Computer and Information Science, vol 767. Springer, Cham. https://doi.org/10.1007/978-3-319-67162-8_28
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DOI: https://doi.org/10.1007/978-3-319-67162-8_28
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