The Development and Validation of the Programming Anxiety Scale
Keywords:
computer programming, programming anxiety, scale development, scale validationAbstract
The main goal of the current study is to develop a reliable instrument to measure programming anxiety in university students. A pool of 33 items based on extensive literature review and experts' opinions were created by researchers. The draft scale comprised three factors applied to 392 university students from two different universities in Turkey. Exploratory and confirmatory factor analysis was conducted on the draft scale. According to analysis results, the Programing Anxiety Scale comprised of two factors and 14 items. In addition, factor loadings for the 14-item scale range from .633 to .918. Internal factor reliability for the whole scale and the subscales was estimated as Cronbach's alpha values of .95, .90, and .94, respectively. In the light of these results, the Programming Anxiety Scale is an appropriate and dependable tool.
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References
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Rountree, N., Rountree, J., & Robins, A. (2002). Predictors of success and failure in a CS1 course. ACM SIGCSE Bulletin, 34(4), 121-124.
Scott, M. (2015). Self-Beliefs in the Introductory Programming Lab and Games-based Fantasy Role-Play (Doctoral dissertation, Brunel University).
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Bennedsen, J., & Caspersen, M. E. (2007). Failure rates in introductory programming. ACM SIGcSE Bulletin, 39(2), 32-36.
Bergin, S., & Reilly, R. (2005). Programming: factors that influence success. In Proceedings of the 36th SIGCSE technical symposium on Computer science education (pp. 411-415).
Berland, M., & Lee, V. R. (2011). Collaborative strategic board games as a site for distributed computational thinking. International Journal of Game-Based Learning (IJGBL), 1(2), 65-81.
Bosch, N., D’Mello, S., & Mills, C. (2013, July). What emotions do novices experience during their first computer programming learning session?. In International Conference on Artificial Intelligence in Education (pp. 11-20). Springer, Berlin, Heidelberg.
Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford Publications.
Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological methods & research, 21(2), 230-258.
Bunderson, E. D., & Christensen, M. E. (1995). An analysis of retention problems for female students in university computer science programs. Journal of Research on Computing in Education, 28(1), 1-18.
Butcher, D. F., & Muth, W. A. (1985). Predicting performance in an introductory computer science course. Communications of the ACM, 28(3), 263-268.
Byrne, P., & Lyons, G. (2001, June). The effect of student attributes on success in programming. In Proceedings of the 6th annual conference on Innovation and technology in computer science education (pp. 49-52).
Chang, S. E. (2005). Computer anxiety and perception of task complexity in learning programming-related skills. Computers in Human Behavior, 21(5), 713-728.
Choo, M. L., & Cheung, K. C. (1991). On meaningful measurement: Junior college pupils' anxiety towards computer programming. Journal of Educational Technology Systems, 19(4), 327-343.
Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319
Connolly, C., Murphy, E., & Moore, S. (2007). Second chance learners, supporting adults learning computer programming. In international conference on engineering education–ICEE.
Connolly, C., Murphy, E., & Moore, S. (2008). Programming anxiety amongst computing students—A key in the retention debate?. IEEE Transactions on Education, 52(1), 52-56.
Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical assessment, research, and evaluation, 10(1), 7.
Dijkstra, E. W. (1989). On the cruelty of really teaching computing science. Communications of the ACM, 32(12), 1398-1404.
Falkner, K., Falkner, N. J., & Vivian, R. (2013, March). Collaborative Learning and Anxiety: A phenomenographic study of collaborative learning activities. In Proceeding of the 44th ACM technical symposium on Computer science education (pp. 227-232).
Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics. Sage
Figueroa Jr, R. B., & Amoloza, E. M. (2015). Addressing Programming Anxiety among Non-Computer Science Distance Learners: A UPOU Case Study. International Journal, 9(1), 56-67.
Gibbs, D. C. (2000). The effect of a constructivist learning environment for field-dependent/independent students on achievement in introductory computer programming. ACM SIGCSE Bulletin, 32(1), 207-211.
Gorsuch, R. L. (1983). Factor analysis (2nd Edition). Hillsdale, NJ: Erlbaum.
Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice-Hall.
Hattie, J. (2008). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. routledge.
Hostetler, T. R. (1983). Predicting student success in an introductory programming course. ACM SIGCSE Bulletin, 15(3), 40-43.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.
Huggard, M. (2004). Programming trauma: can it be avoided. Proceedings of the BCS Grand Challenges in Computing: Education, 50-51.
Jenkins, T. (2002). On the difficulty of learning to program. In Proceedings of the 3rd Annual Conference of the LTSN Centre for Information and Computer Sciences (Vol. 4, No. 2002, pp. 53-58).
Jiang, Y., Zhao, Z., Wang, L., & Hu, S. (2020, August). Research on the Influence of Technology-Enhanced Interactive Strategies on Programming Learning. In 2020 15th International Conference on Computer Science & Education (ICCSE) (pp. 693-697). IEEE.
Jones, P. S., Lee, J. W., Phillips, L. R., Zhang, X. E., & Jaceldo, K. B. (2001). An adaptation of Brislin's translation model for cross-cultural research. Nursing research, 50(5), 300-304.
Kinnunen, P., & Malmi, L. (2006, September). Why students drop out CS1 course?. In Proceedings of the second international workshop on Computing education research (pp. 97-108).
Kinnunen, P., & Simon, B. (2012). My program is ok–am I? Computing freshmen's experiences of doing programming assignments. Computer Science Education, 22(1), 1-28.
Kline, R. B. (1998). Principles and practice of structural equation modeling. New York: Guildford
Maguire, P., Maguire, R., & Kelly, R. (2017). Using automatic machine assessment to teach computer programming. Computer Science Education, 27(3-4), 197-214.
Nunnally, J. C. (1994). Psychometric theory 3E. Tata McGraw-Hill Education.
Orehovacki, T., Radosevic, D., & Konecki, M. (2012, June). Acceptance of Verificator by information science students. In Proceedings of the ITI 2012 34th International Conference on Information Technology Interfaces (pp. 223-230). IEEE.
Owolabi, J., Olanipekun, P., & Iwerima, J. (2014). Mathematics ability and anxiety, computer and programming anxieties, age and gender as determinants of achievement in basic programming. GSTF Journal on Computing (JoC), 3(4), 109.
Özmen, B., & Altun, A. (2014). Undergraduate Students' Experiences in Programming: Difficulties and Obstacles. Turkish Online Journal of Qualitative Inquiry, 5(3).
Pajares, M. F. (1992). Teachers’ beliefs and educational research: Cleaning up a messy construct. Review of educational research, 62(3), 307-332.
Pallant, J. (2007). SPSS survival manual: A step by step guide to data analysis using SPSS version 15 (3rd ed.). New York: Open University Press.
Patil Vivek H, Surendra N. Singh, Sanjay Mishra, and D. Todd Donavan (2017). Parallel Analysis Engine to Aid in Determining Number of Factors to Retain using R [Computer software], available from https://analytics.gonzaga.edu/parallelengine/.
Ramalingam, V., & Wiedenbeck, S. (1998). Development and validation of scores on a computer programming self-efficacy scale and group analyses of novice programmer self-efficacy. Journal of Educational Computing Research, 19(4), 367-381.
Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer science education, 13(2), 137-172.
Rogerson, C., & Scott, E. (2010). The fear factor: How it affects students learning to program in a tertiary environment. Journal of Information Technology Education: Research, 9(1), 147-171.
Rountree, N., Rountree, J., & Robins, A. (2002). Predictors of success and failure in a CS1 course. ACM SIGCSE Bulletin, 34(4), 121-124.
Scott, M. (2015). Self-Beliefs in the Introductory Programming Lab and Games-based Fantasy Role-Play (Doctoral dissertation, Brunel University).
Scott, M. J., & Ghinea, G. (2014, July). Measuring enrichment: the assembly and validation of an instrument to assess student self-beliefs in CS1. In Proceedings of the tenth annual conference on International computing education research (pp. 123-130).
Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Needham Heights, MA: Allyn & Bacon
Wiedenbeck, S., Labelle, D., & Kain, V. N. (2004). Factors affecting course outcomes in introductory programming. In PPIG (p. 11).
Wilson, B. C., & Shrock, S. (2001). Contributing to success in an introductory computer science course: a study of twelve factors. ACM SIGCSE Bulletin, 33(1), 184-188.
Published
2022-05-02
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YILDIRIM, O. G. ., & OZDENER, N. (2022). The Development and Validation of the Programming Anxiety Scale . International Journal of Computer Science Education in Schools, 5(3), 17–34. https://doi.org/10.21585/ijcses.v5i3.140
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