Towards a taxonomy of errors in HTML and CSS

ABSTRACT

As part of a larger research agenda to explore web development as a context for learning computational literacy skills, we investigate errors people make while writing code in HTML and CSS. We report on a lab-based study in which 20 participants were video recorded as they completed coding tasks. We have applied the skills-rules-knowledge framework to segment this data by the cognitive causes of errors they made, and present a taxonomy of these errors. Our findings demonstrate how the skills-rules-framework can be used to analyze coding errors, provide insight about the origins of these errors, and suggest ways that the design of web development tools can be improved to support learning and practice with HTML and CSS.

References

1. Anderson, J. & Jeffries, R. (1985). Novice LISP errors: Undetected losses of information from working memory. Human-Computer Interaction, 1(2), 107--131.
2. Blackwell, A. (2002). First steps in programming: A rationale for attention investment models. HCC, 2--10.
3. Brandt, J., Guo, P., Lewenstein, J., Dontcheva, M., & Klemmer, S. (2009). Two studies of opportunistic programming: Interleaving web foraging, learning, and writing code. CHI. 1589--1598.
4. Bruckman, A. & Edwards, E. (1999). Should we leverage natural-language knowledge?: An analysis of user errors in a natural-language-stye programming language. CHI, 207--214.
5. Désilets, A., Paquet, S., & Vinson, N. (2005). Are wikis usable? WikiSym, 3--15.
6. Dorn, B. & Guzdial, M. (2010). Learning on the job: Characterizing the programming knowledge and learning strategies of web designers. CHI, 703--712.
7. Ko, A. & Myers, B. (2005). A framework and methodology for studying the causes of software errors in programming systems. Journal of Visual Languages and Computing, 16, 41--84.
8. Ko, A. & Wobbrock, J. (2010). Cleanroom: Edit-time error detection with the uniqueness heuristic (pp. 7--14). VL/HCC, 7--14.
9. Miller, C., Perkovic, L., & Settle, A. (2010). File references, trees, and computational thinking. ITiCSE, 132--136.
10. Panko, R. (1998). What we know about spreadsheet errors. Journal of End User Computing, 10(2), 15--21.
11. Park, T., Saxena, A., Jagannath, S., Wiedenbeck, S., & Forte, A. (2013). openHTML: Designing a transitional web editor for novices. CHI Extended Abstracts.
12. Park, T. & Wiedenbeck, S. (2011). Learning web development: Challenges at an earlier stage of computing education. ICER, 125--132.
13. Rasmussen, J. (1983). Skills, rules, and knowledge; Signals, signs, and symbols, and other distinctions in human performance models. IEEE Transactions on Systems, Man, and Cybernetics, 13(3), 257--266.
14. Reason, J. (1990). Human Error. Cambridge University Press.
15. Rosson, M., Ballin, J., & Nash, H. (2004). Everyday programming: Challenges and opportunities for informal web development. VL/HCC, 123--130.
16. Spohrer, J. & Soloway, E. (1986). Alternatives to construct-based programming misconceptions. CHI, 183--191.
17. Strauss, A. and Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. Sage Publications.
18. Youngs, E. (1974). Human errors in programming. International Journal of Man-Machine Studies, 6, 361--376.

Index Terms

• Social and professional topics

  • Professional topics

    • Computing education

      • Computing education programs

        • Computer science education