Abstract
Learning a programming language and solving problems in an algorithmic way is a hard task for many students. A better comprehension of the psychological characteristics involved in this process is needed to reduce these difficulties, implementing teaching methodologies sensitive to these aspects. In this study, we analyse the relationship between cognitive styles, inside the theoretical framework of the empathizing–systemizing (E–S) theory, and performance, also considering the role of sex. In fact, E-S theory states the difference in male and female mind, the first one more empathy-oriented, the second one more oriented at understanding systems. A sample of 56 students attending a course of programming in an Applied Mathematics with a relevant practical activity enhanced with a submission system and supported by a few tutors was involved in the study. We defined profiles of students based on their scores in EQ and SQ, and on their sex, using a cluster analysis. 4 clusters were found: 1. female students with low level of SQ and high level of EQ; 2. female students with high level of both SQ and EQ; 3. male students, with low level of EQ and high level of; 4. male students with low level of both SQ and EQ. A first exploration of the relation of profiles with the learning performance is described.
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References
Ahmed, F., Capretz, L.F., Bouktif, S., Campbell, P.: Soft skills and software development: a reflection from the software industry. Int. J. Inf. Process Manag. 4(3), 171–191 (2013)
Baron-Cohen, S.: The extreme male brain theory of autism. Trends Cogn. Sci. 6(6), 248–254 (2002)
Baron-Cohen, S., Wheelwright, S.: The empathy quotient: an investigation of adults with Asperger syndrome or high functioning autism, and normal sex differences. J. Autism Dev. Disord. 34(2), 163–175 (2004)
Billington, J., Baron-Cohen, S., Wheelwright, S.: Cognitive style predicts entry into physical sciences and humanities: questionnaire and performance tests of empathy and systemizing. Learn. Individ. Differ. 17(3), 260–268 (2007)
Blandin, K., McPeek, R.W., Martin, C.R., Autin, K.: Types of empathy: an experimental study of empathy and Myers-Briggs® types. Center for Applications of Psychological Type, Inc. (2017). https://www.capt.org/journal-psychological-type/jpt-whitepapers.htm. Accessed Feb 2020
Borzovs, J., Kozmina, N., Niedrite, L., Solodovnikova, D., Straujums, U., Zuters, J., Klavins, A.: Can SQ and EQ values and their difference indicate programming aptitude to reduce dropout rate? In: European Conference on Advances in Databases and Information Systems, pp. 285–293. Springer, Cham, September 2017
Briggs-Myers, I., Briggs, K.C.: Myers-Briggs Type Indicator (MBTI). Consulting Psychologists Press, Palo Alto (1985)
Cambridge Autism Research Centre. Empathy Quotient and Systemizing Quotient. http://www.autismresearchcentre.com/arc_tests. Accessed Feb 2020
Coles, M., Phalp, K.T.: Brain type as a programming aptitude predictor. In: PPIG 2016 – 27th Annual Workshop, Cambridge, UK, 7–10 September 2016 (2016)
Digman, J.M.: Personality structure: emergence of the five-factor model. Annu. Rev. Psychol. 41(1), 417–440 (1990)
George, D., Mallery, P.: IBM SPSS Statistics 23 Step by Step: A Simple Guide and Reference. Routledge, London (2016)
Karimi, Z., Wagner, S.: The influence of personality on programming: a summary of a systematic literature review. University of Stuttgart (2014). http://dx.doi.org/10.18419/opus-3243
Li, X., Shih, P.C., Daniel, Y.: Effects of intuition and sensing in programming performance using MBTI personality model. In: Proceedings of the 2nd International Conference on Advances in Image Processing, pp. 189–193, June 2018
Li, X., Shih, P.C., David, E.: The effect of software programmers’ personality on programming performance. In: 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 209–213. IEEE (2018)
Pasini, M., Solitro, U., Brondino, M., Raccanello, D.: The role of the cognitive style in improving the learning to program. In: 27th Annual Workshop of the Psychology of Programming Interest Group, PPIG, pp. 150–155 (2017)
Pasini, M., Solitro, U., Brondino, M., Burro, R., Raccanello, D., Zorzi, M.: Psychology of programming: the role of creativity, empathy and systemizing. In: International Conference in Methodologies and intelligent Systems for Techhnology Enhanced Learning, pp. 82–89. Springer, Cham, June 2017
Rasoal, C., Danielsson, H., Jungert, T.: Empathy among students in engineering programmes. Eur. J. Eng. Educ. 37(5), 427–435 (2012)
Sach, R., Petre, M. Sharp, H.: The Use of MBTI in software engineering. In: 22nd Annual Psychology of Programming Interest Group, September 2010. Universidad Carlos III, Madrid (2010)
Thomson, N.D., Wurtzburg, S.J., Centifanti, L.C.: Empathy or science? Empathy explains physical science enrollment for men and women. Learn. Individ. Differ. 40, 115–120 (2015)
Wakabayashi, A., Baron-Cohen, S., Wheelwright, S., Goldenfeld, N., Delaney, J., Fine, D., Weil, L.: Development of short forms of the Empathy Quotient (EQ-Short) and the Systemizing Quotient (SQ-Short). Pers. Individ. Differ. 41(5), 929–940 (2006)
Wray, S.: SQ minus EQ can predict programming aptitude. In: PPIG, vol. 7, pp. 243–254 (2007)
Maggiolo, S., Mascellani, G.: Introducing CMS: a contest management system. Olympiads Inf. 6, 86–99 (2012)
Solitro, U., Zorzi, M., Pasini, M., Brondino, M.: A “light” application of blended extreme apprenticeship in teaching programming to students of mathematics. In: Methodologies and Intelligent Systems for Technology Enhanced Learning, pp. 73–80. Springer, Cham (2016)
Vihavainen, A., Paksula, M., Luukkainen, M.: Extreme apprenticeship method in teaching programming for beginners. In: Proceedings of the 42nd ACM Technical Symposium on Computer Science Education, pp. 93–98 (2011)
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Solitro, U., Brondino, M., Bonafini, R., Pasini, M. (2020). Profiles in Brain Type in Programming Performance for Non-vocational Courses. In: Vittorini, P., Di Mascio, T., Tarantino, L., Temperini, M., Gennari, R., De la Prieta, F. (eds) Methodologies and Intelligent Systems for Technology Enhanced Learning, 10th International Conference. MIS4TEL 2020. Advances in Intelligent Systems and Computing, vol 1241. Springer, Cham. https://doi.org/10.1007/978-3-030-52538-5_22
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