Examining the Relationship between Digital Game Preferences and Computational Thinking Skills

Year 2017, Volume: 8 Issue: 4, 359 - 369, 18.10.2017

Hatice Yıldız Durak Fatma Gizem Karaoglan Yilmaz , Ramazan Yilmaz

https://izlik.org/JA48SJ88JM

Abstract

The purpose of this study is to identify whether computational thinking skills among secondary school students differ depending on the type of digital games they play. The participants of this study were 202 secondary school students at 5th, 6th, 7th and 8th grades during 2016-2017 academic year. Correlational survey method was used during this study. Furthermore, there were three different data collection instruments used. The first one was “Personal Information Form”. The second one was “Computational Thinking Skills Scale” and the third data collection instrument was “Questionnaire for Type of Games Played with Digital Tools”. Results indicated that students scored higher compared to other sub-scales while their scores from the critical thinking sub-scale was the lowest. The most frequently played game category of the students was found to be dress up/make-up games.

Keywords

Computational thinking; , Digital game types

References

• Anderson, C. A. & Bushman, B. J. (2001). Effects of violent video games on aggressive behavior, aggressive cognition, aggressive affect, physiological arousal, and prosocial behavior: A meta-analytic review of the scientific literature. Psychological Science, 12(5), 353-359.
• Barr, V. & Stephenson, C. (2011). Bringing computational thinking to K-12: what is Involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48-54.
• Boyle, E., Connolly, T. M., & Hainey, T. (2011). The role of psychology in understanding the impact of computer games. Entertainment Computing, 2(2), 69-74.
• Brennan, K. & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Proceedings of the April 2012 annual meeting of the American Educational Research Association, Vancouver, Canada (pp. 1-25).
• Chen, H., Wigand, R. T., & Nilan, M. S. (1999). Optimal experience of web activities. Computers in Human Behavior, 15(5), 585-608.
• Connolly, T. M., Boyle, E. A., MacArthur, E., Hainey, T., & Boyle, J. M. (2012). A systematic literature review of empirical evidence on computer games and serious games. Computers & Education, 59(2), 661-686.
• Connolly, T., Stansfield, M. H., & Hainey, T. (2008). Development of a general framework for evaluating games-based learning. Proceedings of the 2nd European conference on games-based learning (pp. 105-114). Universitat Oberta de Catalunya Barcelona, Spain.
• De Freitas, S. (2006). Learning in immersive worlds. London: Joint Information Systems Committee. Ferguson, C. J. (2007). The good, the bad and the ugly: A meta-analytic review of positive and negative effects of violent video games. Psychiatric Quarterly, 78(4), 309-316.
• Griffiths, M. D., Davies, M. N., & Chappell, D. (2004). Demographic factors and playing variables in online computer gaming. CyberPsychology & Behavior, 7(4), 479-487.
• Grover, S. & Pea, R. (2013). Computational thinking in k–12 a review of the state of the field. Educational Researcher, 42(1), 38-43.
• Hoffman, D. L. & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. The Journal of Marketing, 60(3), 50-68.
• ISTE (2015). CT Leadership toolkit. Retrieved on 19 January 2017 from http://www.iste.org/ docs/ct-documents/ctleadershipt-toolkit.pdf?sfvrsn=4.
• Jeffries, P. R. (2005). A framework for designing, implementing, and evaluating: Simulations used as teaching strategies in nursing. Nursing Education Perspectives, 26(2), 96-103.
• Karasar, N. (2015). Scientific research method. Ankara: Nobel.
• Kazimoglu, C., Kiernan, M., Bacon, L., & MacKinnon, L. (2012). Learning programming at the computational thinking level via digital game-play. Procedia Computer Science, 9, 522-531.
• Kim, B., Park, H., & Baek, Y. (2009). Not just fun, but serious strategies: Using meta-cognitive strategies in game-based learning. Computers & Education, 52(4), 800-810.
• Korkmaz, O., Cakir, R., & Ozden, M. Y. (2015). Computational thinking levels scale (CTLS) adaptation for secondary school level. Gazi Journal of Educational Science, 1(2), 143-162.
• Lee, C. Y. & Chen, M. P. (2009). A computer game as a context for non-routine mathematical problem solving: The effects of type of question prompt and level of prior knowledge. Computers & Education, 52(3), 530-542.
• Liu, C. C., Cheng, Y. B., & Huang, C. W. (2011). The effect of simulation games on the learning of computational problem solving. Computers & Education, 57(3), 1907-1918.
• Ogletree, S. M. & Drake, R. (2007). College students’ video game participation and perceptions: Gender differences and implications. Sex Roles, 56(7-8), 537-542.
• Papastergiou, M. (2009). Digital game-based learning in high school computer science education: Impact on educational effectiveness and student motivation. Computers & Education, 52(1), 1-12.
• Pinto, P. R. (1999). A computer simulation designed for problem‐based learning. Medical Education, 33(1), 47-54.
• Rivers, R. H. & Vockell, E. (1987). Computer simulations to stimulate scientific problem solving. Journal of Research in Science Teaching, 24(5), 403-415.
• Shih, J. L., Shih, B. J., Shih, C. C., Su, H. Y., & Chuang, C. W. (2010). The influence of collaboration styles to children’s cognitive performance in digital problem-solving game “William Adventure”: A comparative case study. Computers & Education, 55(3), 982-993.
• Subrahmanyam, K. & Greenfield, P. M. (1994). Effect of video game practice on spatial skills in girls and boys. Journal of Applied Developmental Psychology, 15(1), 13-32.
• Tan, J. & Biswas, G. (2007). Simulation-based game learning environments: Building and sustaining a fish tank. In Digital Game and Intelligent Toy Enhanced Learning, 2007. DIGITEL'07. The First IEEE International Workshop on (pp. 73-80). IEEE.
• The National Research Council (NRC) (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. The National Academies Press.
• Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
• Yaman, M., Nerdel, C., & Bayrhuber, H. (2008). The effects of instructional support and learner interests when learning using computer simulations. Computers & Education, 51(4), 1784-1794.