The cognition of programming: logical reasoning, algebra and vocabulary skills predict programming performance following an introductory computing course

References

• Armoni, M. (2013). On teaching abstraction in CS to novices. Journal of Computers in Mathematics and Science Teaching, 32(3), 265–284. https://www.learntechlib.org/primary/p/41271/.
   Google Scholar
• Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The autism-spectrum quotient (AQ): Evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders, 31(1), 5–17. https://doi.org/10.1023/A:1005653411471
   PubMed Web of Science ®Google Scholar
• Bennedsen, J., & Caspersen, M. E. (2005, October). An investigation of potential success factors for an introductory model-driven programming course. Proceedings of the First International Workshop on Computing Education Research (pp. 155–163).
   Google Scholar
• European Schoolnet. (2015, November 30). Computing our future. Computer programming and coding. Priorities, school curricula and initiatives across Europe. http://www.eun.org/c/document_library/get_file?uuid=3596b121-941c-4296-a760-0f4e4795d6fa&groupId=43887.
   Google Scholar
• Fedorenko, E., Ivanova, A., Dhamala, R., & Bers, M. U. (2019). The language of programming: A cognitive perspective. Trends in Cognitive Sciences, 23(7), 525–528. https://doi.org/10.1016/j.tics.2019.04.010
   PubMed Web of Science ®Google Scholar
• Floyd, B., Santander, T., & Weimer, W. (2017). Decoding the representation of code in the brain: An fMRI study of code review and expertise. 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE) (pp. 175–186). https://doi.org/10.1109/ICSE.2017.24.
   Google Scholar
• Graafsma, I. L., Robidoux, S., Nickels, L., Roberts, M., & Marinus, E. (2020). Validating two short versions of the second computer science 1 programming ability test. Open Science Framework. https://osf.io/exkbm/?view_only=719e21fee326407cad1b39fbfb866d80.
   Google Scholar
• Guzdial, M. (2003). A media computation course for non-majors. Proceedings of the 8th Annual Conference on Innovation and Technology in Computer Science Education (pp. 104–108). https://doi.org/10.1145/961511.961542.
   Google Scholar
• Guzdial, M. (2016). Synthesis lectures on human-centered informatics. Synthesis Lectures on Human-Centered Informatics, 8(6), 1–165. https://doi.org/10.1007/978-3-031-02216-6
   Google Scholar
• Guzdial, M., & du Boulay, B. (2019). The history of computing education research. In S.A. Fischer & A.V. Robins (Eds.), The Cambridge handbook of computing education research (pp. 11–39). Cambridge University Press.
   Google Scholar
• Hackerrank. (2018, January 23). Developer skills report. http://research.hackerrank.com/developer-skills/2018/.
   Google Scholar
• Handley, S. J., Capon, A., Copp, C., & Harper, C. (2002). Conditional reasoning and the tower of Hanoi: The role of spatial and verbal working memory. British Journal of Psychology, 93(4), 501–518. https://doi.org/10.1348/000712602761381376
   PubMedGoogle Scholar
• Hermans, F., & Aldewereld, M. (2017). Programming is writing is programming. Science and Engineering of Programming, 1, 1–8. https://doi.org/10.1145/3079368.3079413.
   Google Scholar
• IBM. (1968). Aptitude test for programmer personnel.
   Google Scholar
• Jacob, S. R., & Warschauer, M. (2018). Computational thinking and literacy. Journal of Computer Science Integration, 1(1), https://doi.org/10.26716/jcsi.2018.01.1.1
   Google Scholar
• JASP Team. (2019). JASP (Version 0.10.2) [Computer software].
   Google Scholar
• Kelleher, C., & Pausch, R. (2003). Lowering the barriers to programming. ACM Computing.
   Google Scholar
• Kline, R. (2005). Principles and practices of structural equation modeling (2nd ed.). Guilford Press.
   Google Scholar
• Marasco, E. A., Moshirpour, M., & Moussavi, M. (2017). Flipping the foundation: A multi-year flipped classroom study for a large-scale introductory programming course [Paper presentation]. ASEE Annual Conference & Exposition, Columbus, Ohio, USA. https://peer.asee.org/28372.
   Google Scholar
• O’Regan, G. (2012). History of programming languages. In G. O'Regan (Ed.), A brief history of computing (pp. 121–144). Springer. https://doi.org/10.1007/978-1-4471-2359-0_9.
   Google Scholar
• Pandža, N. B. (2016). Computer programming as a second language. In N.B. Pandža (Ed.), Advances in human factors in cybersecurity (pp. 439–445). Springer. https://doi.org/10.1007/978-3-319-41932-9_36.
   Google Scholar
• Parker, M. C., Guzdial, M., & Engleman, S. (2016). Replication, validation, and use of a language independent CS1 knowledge assessment. Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 93–101). https://doi.org/10.1145/2960310.2960316.
   Google Scholar
• Paulson, L. D. (2007). Developers shift to dynamic programming languages. Computer, 40(2), 12–15. https://doi.org/10.1109/MC.2007.53
   Web of Science ®Google Scholar
• Pea, R. D., & Kurland, D. M. (1984). On the cognitive effects of learning computer programming. New Ideas in Psychology, 2(2), 137–168. https://doi.org/10.1016/0732-118X(84)90018-7
   Web of Science ®Google Scholar
• Pena, C. M., & Tirre, W. C. (1992). Cognitive factors involved in the first stage of programming skill acquisition. Learning and Individual Differences, 4(4), 311–334. https://doi.org/10.1016/1041-6080(92)90017-9
   Google Scholar
• Perrenet, J., Groote, J. F., & Kaasenbrood, E. (2005). Exploring students' understanding of the concept of algorithm: levels of abstraction. ACM SIGCSE Bulletin, 37(3), 64–68.
   Google Scholar
• Pimsleur, P., Reed, D. J., & Stansfield, C. W. (2004). Pimsleur language aptitude battery: Manual 2004 edition. Second Language Testing.
   Google Scholar
• Polito, V., Barnier, A. J., & Woody, E. Z. (2013). Developing the sense of agency rating scale (SOARS): An empirical measure of agency disruption in hypnosis. Consciousness and Cognition, 22(3), 684–696. https://doi.org/10.1016/j.concog.2013.04.003
   PubMed Web of Science ®Google Scholar
• Prat, C. S., Madhyastha, T. M., Mottarella, M. J., & Kuo, C.-H. (2020). Re-epithelialization and immune cell behaviour in an ex vivo human skin model. Scientific Reports, 10(1), 1–10. https://doi.org/10.1038/s41598-019-56847-4
   PubMed Web of Science ®Google Scholar
• Quille, K., & Bergin, S. (2018). Programming: Predicting student success early in CS1. A re-validation and replication study. In Proceedings of the 23rd annual ACM conference on innovation and technology in computer science education (pp. 15–20). https://doi.org/10.1145/3197091.3197101.
   Google Scholar
• Quille, K., & Bergin, S. (2019). CS1: how will they do? How can we help? A decade of research and practice. Computer Science Education, 29(2-3), 254–282. https://doi.org/10.1080/08993408.2019.1612679
   Web of Science ®Google Scholar
• R Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing. URL https://www.R-project.org/.
   Google Scholar
• Rogers, V., Meara, P., Barnett-Legh, T., Curry, C., & Davie, E. (2017). Examining the LLAMA aptitude tests. Journal of the European Second Language Association, 1(1), 49–60. https://doi.org/10.22599/jesla.24.
   Google Scholar
• Romanova, A. (2015). Word class effects on representation and processing in non-brain damaged speakers and people with aphasia. [Doctoral dissertation, Macquarie University]. Macquarie University Library. https://www.researchonline.mq.edu.au/vital/access/services/Download/mq:44500/SOURCE1?view=true.
   Google Scholar
• Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237. https://doi.org/10.3758/PBR.16.2.225
   PubMed Web of Science ®Google Scholar
• Rushkoff. (2012). Code literacy: A 21st-century requirement. Edutpia. https://www.edutopia.org/blog/code-literacy-21st-century-requirement-douglas-rushkoff.
   Google Scholar
• Seegerer, S., Michaeli, T., & Romeike, R. (2019). Informatik für alle-Eine Analyse von Argumenten und Argumentationsschemata für das Schulfach Informatik [Computer science for everyone – an analysis of arguments and argumentation schemes for the school subject computer science.]. INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik–Informatik für Gesellschaft. https://doi.org/10.18420/inf2019_77.
   Google Scholar
• Shute, V. J. (1991). Who is likely to acquire programming skills? Journal of Educational Computing Research, 7(1), 1–24. https://doi.org/10.2190/VQJD-T1YD-5WVB-RYPJ
   Web of Science ®Google Scholar
• Siegmund, J., Kästner, C., Apel, S., Parnin, C., Bethmann, A., Leich, T., Saake, G., & Brechmann, N. (2014). Understanding understanding source code with functional magnetic resonance imaging. In Proceedings of the 36th International Conference on Software Engineering (pp. 378–389). https://doi.org/10.1145/2568225.2568252.
   Google Scholar
• Skehan, P. (1991). Individual differences in second language learning. Studies in Second Language Acquisition, 13(2), 275–298. https://doi.org/10.1017/S0272263100009979
   Google Scholar
• Vee, A. (2013). Understanding computer programming as a literacy. Literacy in Composition Studies, 1(2), 42–64. https://doi.org/10.21623/1.1.2.4
   Google Scholar
• Vogel, S., Hoadley, C., Ascenzi-Moreno, L., & Menken, K. (2019). The role of translanguaging in computational literacies. Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 1164–1170). https://doi.org/10.1145/3287324.3287368.
   Google Scholar
• Webb, N. M. (1985). Cognitive requirements of learning computer programming in group and individual settings. AEDS Journal, 18(3), 183–194. https://doi.org/10.1080/00011037.1985.11008398
   Google Scholar