Abstract
Many first-year students of Computer Science struggle when attempting to learn software development. If as teachers, we want to effectively help our students to overcome this hurdle, we need to better understand the main causes for these difficulties. To gain a systematic insight, we merge data we gathered from a test instrument that assesses the competence of abstract thinking, with the students’ performance data as documented within our student administration system. On this basis, we apply methods from data analysis to detect correlations between the skill level in abstract thinking and the overall study success in our degree program on Computer Science.
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References
Böttcher, A., Thurner, V., Häfner, T.: Applying data analysis to identify early indicators for potential risk of dropout in CS students. In: 2020 IEEE Global Engineering Education Conference (EDUCON), pp. 827–836 (2020)
Böttcher, A., Thurner, V., Häfner, T., Hertle, J.: A data science-based approach for identifying counseling needs in first-year students. In: 2021 IEEE Global Engineering Education Conference (EDUCON), pp. 420–429 (2021)
Bühner, M.: Einführung in die Test- und Fragebogenkonstruktion. Pearson Deutschland GmbH (2011)
Koschmieder, C., Weissenbacher, B., Riegler, D., Krammer, G., Gruber, C., Neubauer, A.: What can psychological tests contribute beyond GPA? findings on predictive validity, self- and other-selection in admission procedures in higher education. Diagnostica 68(2), 88–100 (2022)
Kothe, C., Hissbach, J., Hampe, W.: Prediction of practical performance in preclinical laboratory courses – the return of wire bending for admission of dental students in Hamburg. GMS Z. Med. Ausbildung 31(22) (2014)
Kramer, J.: Is abstraction the key to computing. Commun. ACM 50(4), 36–42 (2007)
Krings, R., Huwendiek, S., Walsh, N., Stricker, D., Berendonk, C.: Predictive power of high school educational attainment and the medical aptitude test for performance during the Bachelor program in human medicine at the university of Bern: a cohort study. Swiss Med. Weekly 150(51), w20389 (2020)
Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)
Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., Loumos, V.: Dropout prediction in e-learning courses through the combination of machine learning techniques. Comput. Educ. 53(3), 950–965 (2009)
Meyer, H., Zimmermann, S., Hissbach, J., Klusmann, D., Hampe, W.: Selection and academic success of medical students in Hamburg, Germany. BMC Med. Educ. 19(23), 1–15 (2019)
Söderlind, J., Geschwind, L.: More students of better quality? effects of a mathematics and physics aptitude test on student performance. Eur. J. Eng. Educ. 42, 445–457 (2017)
Wing, J.M.: Computational thinking. Commun. ACM 49(3), 33–35 (2006)
Yao, H., Lian, D., Cao, Y., Wu, Y., Zhou, T.: Predicting academic performance for college students: a campus behavior perspective. ACM Trans. Intell. Syst. Technol. 10(3), 1–21 (2019)
Zehetmeier, D.: Computer Science and Abstract Thinking – Concept, Assessment, Training. PhD thesis, Technical University of Munich (2019)
Zehetmeier, D., Böttcher, A., Brüggemann-Klein, A., Thurner, V.: Defining the competence of abstract thinking and evaluating cs-students’ level of abstraction. In Bui, T. (ed.) 52nd Hawaii International Conference on System Sciences, HICSS 2019, Grand Wailea, Maui, Hawaii, USA, 8-11 January 2019, pp. 1–10. ScholarSpace (2019)
Zehetmeier, D., Böttcher, A., Thurner, V.: Design and evaluation of a test for assessing CS-first-year students’ cognitive competences. In: IEEE Global Engineering Education Conference, EDUCON, pp. 726–734 (2017)
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Böttcher, A., Thurner, V., Zehetmeier, D., Häfner, T. (2023). Impact of Students’ Initial Abstract Thinking Competence on Successfully Studying Computer Science. In: Auer, M.E., Pachatz, W., Rüütmann, T. (eds) Learning in the Age of Digital and Green Transition. ICL 2022. Lecture Notes in Networks and Systems, vol 634. Springer, Cham. https://doi.org/10.1007/978-3-031-26190-9_54
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DOI: https://doi.org/10.1007/978-3-031-26190-9_54
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