Thinking out of the box: comparing metaphors for variables in programming education

ABSTRACT

When teaching novices programming, misconceptions can occur. Misconception are incorrect beliefs about certain programming concept. For example, some novices think that a variable can hold multiple values, in the case of two consecutive assignment statements, such as x = 5; x = 7. While explaining variables introductory materials often use the metaphor of a box for a variable, which might contribute to the 'multiple values' hypothesis. To investigate this, we design and run a controlled experiment with 496 novice programmers, both children and adults. Half of our participants receive an introductory programming lesson in which we explain a variable as a box, while the other half of participants receive the explanation of a variable as being a label. They are subsequently questioned about their understanding of variables. Our results show that, for the simple questions involving one assignment, the box group performs better. However, for questions involving the misconception --- with two consecutive assignment statements --- the label group outperforms the box group. This however primarily occurs when considering variables of type string, for integers subjects interpret the statements as numeric values to be added.

References

1. Efthimia Aivaloglou and Felienne Hermans. 2016. How Kids Code and How We Know: An Exploratory Study on the Scratch Repository. In Proceedings of the 2016 ACM Conference on International Computing Education Research (ICER '16). ACM, 53--61.
2. Benedict Du Boulay. 1986. Some Difficulties of Learning to Program. Journal of Educational Computing Research 2, 1 (1986), 57--73.
3. Karen Brennan, Christian Balch, and Michelle Chung. 2014. CREATIVE COMPUTING. Harvard Graduate School of Education.
4. D. Doukakis, M. Grigoriadou, and G Tsaganou. 2007. Understanding the Programming Variable Concept with Animated Interactive Analogies. Proceedings of the The 8th Hellenic European Research on Computer Mathematics & its Applications Conference, HERCMA '07 (2007).
5. Deborah A. Fields, Michael Giang, and Yasmin Kafai. 2014. Programming in the Wild: Trends in Youth Computational Participation in the Online Scratch Community. In Proceedings of the 9th Workshop in Primary and Secondary Computing Education (WiPSCE '14). ACM, 2--11.
6. Pat Fung, Mike Brayshaw, Benedict Du Boulay, and Mark Elsom-Cook. 1990. Towards a taxonomy of novices' misconceptions of the Prolog interpreter. Instructional Science 19, 4--5 (1990), 311--336.
7. Ken Goldman, Paul Gross, Cinda Heeren, Geoffrey Herman, Lisa Kaczmarczyk, Michael C. Loui, and Craig Zilles. 2008. Identifying important and difficult concepts in introductory computing courses using a delphi process. ACM SIGCSE Bulletin 40, 1 (2008), 256.
8. Simon Holland, Robert Griffiths, and Mark Woodman. 1997. Avoiding Object Misconceptions. SIGCSE Bull. 29, 1 (March 1997), 131--134.
9. Linxiao Ma. 2007. Investigating and improving novice programmers’ mental models of programming concepts. Ph.D. Dissertation. University of Strathclyde, Glasgow, UK. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444415
10. L. Ma, J. Ferguson, M. Roper, and M. Wood. 2011. Investigating and improving the models of programming concepts held by novice programmers. Computer Science Education 21, 1 (2011), 57--80.
11. John H. Maloney, Kylie Peppler, Yasmin Kafai, Mitchel Resnick, and Natalie Rusk. 2008. Programming by Choice: Urban Youth Learning Programming with Scratch. In Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education (SIGCSE '08). ACM, 367--371.
12. Orni Meerbaum-Salant, Michal Armoni, and Mordechai (Moti) Ben-Ari. 2010. Learning Computer Science Concepts with Scratch. In Proceedings of the Sixth International Workshop on Computing Education Research (ICER '10). ACM, New York, NY, USA, 69--76.
13. J. Moreno and G. Robles. 2014. Automatic detection of bad programming habits in scratch: A preliminary study. In 2014 IEEE Frontiers in Education Conference (FIE). 1--4.
14. Ralph T. Putnam, D. Sleeman, Juliet A. Baxter, and Laiani K. Kuspa. 1986. A Summary of Misconceptions of High School Basic Programmers. Journal of Educational Computing Research 2, 4 (1986), 459--472.
15. Mitchel Resnick, John Maloney, Andrés Monroy-Hernández, Natalie Rusk, Evelyn Eastmond, Karen Brennan, Amon Millner, Eric Rosenbaum, Jay Silver, Brian Silverman, and Yasmin Kafai. 2009. Scratch: Programming for All. Commun. ACM 52, 11 (Nov. 2009), 60--67.
16. Linda Seiter and Brendan Foreman. 2013. Modeling the Learning Progressions of Computational Thinking of Primary Grade Students. In Proceedings of the Ninth Annual International ACM Conference on International Computing Education Research. ACM, 59--66.
17. D. Sleeman, Ralph T. Putnam, Juliet Baxter, and Laiani Kuspa. 1986. Pascal and High School Students: A Study of Errors. Journal of Educational Computing Research 2, 1 (1986), 5--23.
18. Elliot Soloway, Kate Ehrlich, Jeffrey Bonar, and Judith Greenspan. 1982. What do novices know about programming. Directions in human-computer interaction (1982), 87--122.
19. Juha Sorva. 2008. The same but different students' understandings of primitive and object variables. Proceedings of the 8th International Conference on Computing Education Research - Koli '08 (2008).
20. Juha Sorva. 2012. Visual program simulation in introductory programming education; Visuaalinen ohjelmasimulaatio ohjelmoinnin alkeisopetuksessa. (2012), 428 pages. http://urn.fi/URN:ISBN:978-952-60-4626-6
21. Amanda Wilson, Thomas Hainey, and Thomas Connolly. 2012. Evaluation of computer games developed by primary school children to gauge understanding of programming concepts. In European Conference on Games Based Learning. Academic Conferences International Limited, 549.
22. Seungwon Yang, Carlotta Domeniconi, Matt Revelle, Mack Sweeney, Ben U. Gelman, Chris Beckley, and Aditya Johri. 2015. Uncovering Trajectories of Informal Learning in Large Online Communities of Creators. In Proceedings of the Second ACM Conference on Learning @ Scale. ACM, 131--140.

Index Terms

• Social and professional topics

  • Professional topics

    • Computing education

      • Computational thinking

      • K-12 education