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The Correctness of the Mental Model of Arrays After Instruction for CS1 Students

Published:07 March 2024Publication History

ABSTRACT

Researchers stipulate that a mental model of a system comprises two types of sub-components: parts andstate changes. CS education researchers have noted that state changes in a program are some of the most troublesome concepts to understand. Furthermore, challenges understanding a program's dynamic state changes persist in beginning students even after instruction. Drawing from the theories of mental models, we decomposed arrays into its sub-components of parts andstate changes. Using a questionnaire, we elicited CS1 students' mental models after they received instruction on arrays and then we analyzed and compared the correctness of their mental models with a focus on this decomposition. We compared the mental model correctness of the parts and state changes components. We found that the participants' mental model correctness of parts was significantly higher (i.e., more correct responses) than the mental model correctness of state changes, regardless of teaching modality (online or in-person) or prior programming experience. Moreover, participants with prior programming experience have higher mental model correctness (both for parts and state changes) than participants with no prior programming experience. We close with a discussion of the implications of these findings for introductory courses and highlight recommendations from the literature on ways to teach dynamic aspects of programming.

References

  1. John M Carroll and Judith Reitman Olson. 1988. Mental models in humancomputer interaction. Handbook of human-computer interaction (1988), 45--65.Google ScholarGoogle Scholar
  2. KJW Craik. 1943. The Nature of Explanation Cambridge University Press: Cambridge.Google ScholarGoogle Scholar
  3. Kathryn Cunningham. 2020. Purpose-first programming: A programming learning approach for learners who care most about what code achieves. In Proceedings of the 2020 ACM Conference on International Computing Education Research. 348-- 349.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Johan de Kleer and John Seely Brown. 1985. Qualitative Reasoning about Physical Systems, chapter Qualitative Physics Based on Confluences.Google ScholarGoogle Scholar
  5. Saeed Dehnadi. 2009. A cognitive study of learning to program in introductory programming courses. Ph.D. Dissertation. Middlesex University.Google ScholarGoogle Scholar
  6. James K Doyle, David N Ford, Michael J Radzicki, andWScott Trees. 2001. Mental models of dynamic systems. Encyclopedia of Life Support Systems (2001).Google ScholarGoogle Scholar
  7. Benedict Du Boulay. 1986. Some difficulties of learning to program. Journal of Educational Computing Research 2, 1 (1986), 57--73.Google ScholarGoogle ScholarCross RefCross Ref
  8. Barbara Ericson and Beryl Hoffman. [n. d.]. AP CSA Java Course x2014; AP CSAwesome - runestone.academy. https://runestone.academy/ns/books/ published/csawesome/index.html. [Accessed 05-08--2023].Google ScholarGoogle Scholar
  9. Sally Fincher, Johan Jeuring, Craig S Miller, Peter Donaldson, Benedict Du Boulay, Matthias Hauswirth, Arto Hellas, Felienne Hermans, Colleen Lewis, Andreas Mühling, et al. 2020. Capturing and characterising notional machines. In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education. 502--503.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Ken Goldman, Paul Gross, Cinda Heeren, Geoffrey L Herman, Lisa Kaczmarczyk, Michael C Loui, and Craig Zilles. 2010. Setting the scope of concept inventories for introductory computing subjects. ACM Transactions on Computing Education (TOCE) 10, 2 (2010), 1--29.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Mark Guzdial and Barbara Ericson. 2007. Introduction to computing & programming in Java: a multimedia approach. Pearson Prentice Hall.Google ScholarGoogle Scholar
  12. Frank G Halasz and Thomas P Moran. 1983. Mental models and problem solving in using a calculator. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems. 212--216.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. PN Johnson-Laird, B Gawronski, and Fritz Strack. 2012. Mental models and consistency. Cognitive consistency: A fundamental principle in social cognition (2012), 225--243.Google ScholarGoogle Scholar
  14. Natalie A Jones, Helen Ross, Timothy Lynam, Pascal Perez, and Anne Leitch. 2011. Mental models: an interdisciplinary synthesis of theory and methods. Ecology and Society 16, 1 (2011).Google ScholarGoogle Scholar
  15. Lisa C Kaczmarczyk, Elizabeth R Petrick, J Philip East, and Geoffrey L Herman. 2010. Identifying student misconceptions of programming. In Proceedings of the 41st ACM technical symposium on Computer science education. ACM, 107--111.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. David E Kieras and Susan Bovair. 1984. The role of a mental model in learning to operate a device. Cognitive science 8, 3 (1984), 255--273.Google ScholarGoogle Scholar
  17. Shriram Krishnamurthi and Kathi Fisler. 2019. Programming paradigms and beyond. The Cambridge Handbook of Computing Education Research 37 (2019).Google ScholarGoogle ScholarCross RefCross Ref
  18. Colleen M Lewis. 2021. Physical Java Memory Models: A Notional Machine. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education. 383--389.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Violetta Lonati, Andrej Brodnik, Tim Bell, Andrew Paul Csizmadia, Liesbeth De Mol, Henry Hickman, Therese Keane, Claudio Mirolo, and Mattia Monga. 2022. What we talk about when we talk about programs. In Proceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education. 117--164.Google ScholarGoogle Scholar
  20. Linxiao Ma. 2007. Investigating and improving novice programmers' mental models of programming concepts. Ph.D. Dissertation. University of Strathclyde.Google ScholarGoogle Scholar
  21. Richard Mayer and Joan K. Gallini. 1990. When Is an Illustration Worth Ten Thousand Words? Journal of Educational Psychology 82 (12 1990), 715--726.Google ScholarGoogle Scholar
  22. Syeda Fatema Mazumder, Celine Latulipe, and Manuel A Pérez-Quiñones. 2020. Are variable, array and object diagrams in java textbooks explanative?. In Proceedings of the 2020 ACM conference on innovation and technology in computer science education. 425--431.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Donald A Norman. 1983. Some observations on mental models. In Mental models. Psychology Press, 15--22.Google ScholarGoogle Scholar
  24. Janet Rountree and Nathan Rountree. 2009. Issues regarding threshold concepts in computer science. In Proceedings of the Eleventh Australasian Conference on Computing Education-Volume 95. 139--146.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Kate Sanders and Robert McCartney. 2016. Threshold concepts in computing: past, present, and future. In Proceedings of the 16th Koli Calling international conference on computing education research. 91--100.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Dermot Shinners-Kennedy. 2008. The everydayness of threshold concepts: State as an example from computer science. In Threshold concepts within the disciplines. Brill, 119--128.Google ScholarGoogle Scholar
  27. Teemu Sirkiä and Juha Sorva. 2012. Exploring programming misconceptions: an analysis of student mistakes in visual program simulation exercises. In Proceedings of the 12th Koli Calling International Conference on Computing Education Research. 19--28.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Paul E Smaldino. 2017. Models are stupid, and we need more of them. Computational social psychology (2017), 311--331.Google ScholarGoogle Scholar
  29. Juha Sorva. 2008. The Same but Different Students' Understandings of Primitive and Object Variables. In Proceedings of the 8th International Conference on Computing Education Research (Koli '08). ACM, New York, NY, USA, 5--15.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Juha Sorva. 2010. Reflections on threshold concepts in computer programming and beyond. In Proceedings of the 10th Koli calling international conference on computing education research. 21--30.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Juha Sorva. 2013. Notional Machines and Introductory Programming Education. Trans. Comput. Educ. 13, 2 (July 2013). https://doi.org/10.1145/2483710.2483713Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Juha Sorva. 2018. Misconceptions and the Beginner Programmer. In Computer Science Education: Perspectives on Teaching and Learning in School.Google ScholarGoogle Scholar
  33. Preston Tunnell Wilson, Kathi Fisler, and Shriram Krishnamurthi. 2018. Evaluating the tracing of recursion in the substitution notional machine. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education. 1023--1028.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Evgenia Vagianou. 2006. Program working storage: a beginner's model. In Proceedings of the 6th Baltic Sea conference on Computing education research: Koli Calling 2006. 69--76.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Theo B Weidmann, Sverrir Thorgeirsson, and Zhendong Su. 2022. Bridging the Syntax-Semantics Gap of Programming. In Proceedings of the 2022 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software. 80--94.Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Conferences
    SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1
    March 2024
    1583 pages
    ISBN:9798400704239
    DOI:10.1145/3626252

    Copyright © 2024 ACM

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    Publication History

    • Published: 7 March 2024

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