Abstract
There are myriad factors which can affect a student’s academic performance as measured by Grade Point Average (GPA). Identifying characteristics of students with high GPA can help more students understand how to achieve the best grades possible. In this paper, a variety of data mining algorithms are used to predict the GPA of undergraduate students majoring in Computer Science based on survey questions. The results demonstrate that the number of hours of sleep per night, the frequency of illicit drug use, the number of hours spent studying per week, and the number of hours spent on social media platforms per week are important factors that can be used to classify student GPA. The Random Forest data mining algorithm performed the best and was able to achieve a predictive accuracy of 95% when placing students into one of four academic performance groupings.
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References
G. Curcio, M. Ferrara, L. De Gennaro, Sleep loss, learning capacity and academic performance. Sleep Med Rev. 10(5), 323–337 (2006)
R. Gruber, R. Laviolette, P. Deluca, E. Monson, K. Cornish, J. Carrier, Short sleep duration is associated with poor performance on IQ measures in healthy school-age children. Sleep Med. 11(3), 289–294 (2010)
K. Ahrberg, M. Dresler, S. Niedermaier, A. Steiger, L. Genzel, The interaction between sleep quality and academic performance. J Psychiatr Res. 46(12), 1618–1622 (2012)
A.H. Eliasson, C.J. Lettieri, A.H. Eliasson, Early to bed, early to rise! Sleep habits and academic performance in college students. Sleep Breath. 14, 71–75 (2010)
I.M. Verweij, N. Romeijn, D.J. Smit, et al., Sleep deprivation leads to a loss of functional connectivity in frontal brain regions. BMC Neurosci. 15, 88 (2014)
Harvard Health Publishing, Harvard Medical School. Positive outlook may mean better sleep https://www.health.harvard.edu/staying-healthy/positive-outlook-may-mean-better-sleep
PLOS Research News, Alcohol and marijuana use associated with lower GPA in college. https://researchnews.plos.org/2017/03/08/alcohol-and-marijuana-use-associated-with-lower-gpa-in-college/
Science Daily, Who learns foreign language better, introverts or extroverts? https://www.sciencedaily.com/releases/2017/07/170721104246.htm
M.A. Al-Barrak, M. Al-Razgan, Predicting students final GPA using decision trees: A case study. Int. J. Inf. Educat. Technol. 6(7), 528–533 (2016)
A. Polyzou, G. Karypis, Feature extraction for classifying students based on their academic performance, in Proceedings of the 11th EDM Conference (2018)
O. Heradstveit, J.C. Skogen, J. Hetland, M. Hysing, Alcohol and illicit drug use are important factors for school-related problems among adolescents. Front. Psychol. 8, 1023 (2017). https://doi.org/10.3389/fpsyg.2017.01023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5476929/
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Khan, F., Weiss, G.M., Leeds, D.D. (2021). Predicting the Academic Performance of Undergraduate Computer Science Students Using Data Mining. In: Arabnia, H.R., Deligiannidis, L., Tinetti, F.G., Tran, QN. (eds) Advances in Software Engineering, Education, and e-Learning. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-70873-3_21
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DOI: https://doi.org/10.1007/978-3-030-70873-3_21
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