nach oben

Education and Information Technologies

Erschienen in:

27.06.2022

Playing coding games to learn computational thinking: What motivates students to use this tool at home?

verfasst von: Shuhan Zhang, Gary K. W. Wong, Peter C. F. Chan

Erschienen in: Education and Information Technologies | Ausgabe 1/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Coding games are widely used to teach computational thinking (CT). Studies have broadly investigated the role of coding games in supporting CT learning in formal classroom contexts, but there has been limited exploration of their use in informal home-based settings. This study investigated the factors that motivated students to use a coding game called Coding Galaxy in a home-based setting. It explored the connections between the students’ perceptions of and usage of the tool. An 11-day intervention was conducted at a primary school in Hong Kong with 104 participants. The students’ perceptions of the game were collected via questionnaires and information on their use of the tool was extracted from log files. Results indicated that coding motivation and feeling of enjoyment were predictors of the actual use of the game, with coding motivation the dominant factor. Focus group interviews were also conducted to further explore the students’ motivation to play the game. Through comparisons of active and inactive users, the qualitative findings supported the quantitative results, indicating that students who were more intrinsically motivated tended to be more active in using the game. The implications of the study for researchers and practitioners in CT education are discussed.

Vorheriger Artikel Systematic literature review and bibliometric analysis on virtual reality and education

Nächster Artikel College education: Problem-solving creativity in an interactive learning environment

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Bachu, E., & Bernard, M. (2011). Enhancing computer programming fluency through game playing. International Journal of Computing, 1(3).

Bers, M. U. (2018). Coding and computational thinking in early childhood: The impact of ScratchJr in Europe. European Journal of STEM Education, 3(3), 8. https://doi.org/10.20897/ejsteme/3868

Bey, A., Pérez-Sanagustín, M., & Broisin, J. (2019). Unsupervised automatic detection of learners’ programming behavior. European Conference on Technology Enhanced Learning.

Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., Engelhardt, K., Kampylis, P., & Punie, Y. (2016). Developing computational thinking in compulsory education. European Commission, JRC Science for Policy Report, 68.

Buffum, P. S., Frankosky, M., Boyer, K. E., Wiebe, E. N., Mott, B. W., & Lester, J. C. (2016). Collaboration and Gender Equity in Game-Based Learning for Middle School Computer Science. Computing in Science & Engineering, 18(2), 18–28. https://doi.org/10.1109/MCSE.2016.37.CrossRef

Çakır, R., Şahin, H., Balci, H., & Vergili, M. (2021). The effect of basic robotic coding in-service training on teachers’ acceptance of technology, self-development, and computational thinking skills in technology use. Journal of Computers in Education, 8(2), 237–265. https://doi.org/10.1007/s40692-020-00178-1CrossRef

Camilleri, A., & Camilleri, M. A. (2019). The students’ perceived use, ease of use and enjoyment of educational games at home and at school. 13th Annual International Technology, Education and Development Conference. Valencia, Spain.

Cheng, Y.-M., Lou, S.-J., Kuo, S.-H., & Shih, R.-C. (2013). Investigating elementary school students’ technology acceptance by applying digital game-based learning to environmental education. Australasian Journal of Educational Technology, 29(1). https://doi.org/10.14742/ajet.65

Daniel, J. (2020). Education and the COVID-19 pandemic. Prospects, 49(1), 91–96. https://doi.org/10.1007/s11125-020-09464-3.CrossRef

Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487. https://doi.org/10.1006/imms.1993.1022CrossRef

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982CrossRef

Feng, C., Wang, H., Lu, N., Chen, T., He, H., & Lu, Y. (2014). Log-transformation and its implications for data analysis. Shanghai Archives of Psychiatry, 26(2), 105.

Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.1177/002224378101800313CrossRef

Giannakos, M. N. (2013). Enjoy and learn with educational games: Examining factors affecting learning performance. Computers & Education, 68, 429–439. https://doi.org/10.1016/j.compedu.2013.06.005CrossRef

Giannakoulas, A., & Xinogalos, S. (2018). A pilot study on the effectiveness and acceptance of an educational game for teaching programming concepts to primary school students. Education and Information Technologies, 23(5), 2029–2052. https://doi.org/10.1007/s10639-018-9702-xCrossRef

Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051CrossRef

Ha, I., Yoon, Y., & Choi, M. (2007). Determinants of adoption of mobile games under mobile broadband wireless access environment. Information & Management, 44(3), 276–286. https://doi.org/10.1016/j.im.2007.01.001CrossRef

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Advanced diagnostics for multiple regression: A supplement to multivariate data analysis. Prentice Hall.

Hsu, C.-L., & Lu, H.-P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41(7), 853–868. https://doi.org/10.1016/j.im.2003.08.014CrossRef

Ibrahim, R., Khalil, K., & Jaafar, A. (2011). Towards educational games acceptance model (EGAM): A revised unified theory of acceptance and use of technology (UTAUT). International Journal of Research and Reviews in Computer Science, 2(3), 839.

Jiang, S., & Wong, G. K. (2017). Assessing primary school students’ intrinsic motivation of computational thinking. In 2017 IEEE 6th International Conference on Teaching, Assessment, and Learning for Engineering (TALE) (pp. 469–474). IEEE. https://doi.org/10.1109/TALE.2017.8252381

Jung, H., Kim, H. J., So, S., Kim, J., & Oh, C. (2019). TurtleTalk: An educational programming game for children with voice user interface. Paper presented at the Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3290607.3312773

Kazimoglu, C., Kiernan, M., Bacon, L., & Mackinnon, L. (2012). A serious game for developing computational thinking and learning introductory computer programming. Procedia-Social and Behavioral Sciences, 47, 1991–1999. https://doi.org/10.1016/j.sbspro.2012.06.938CrossRef

Kim, Y. J., & Shute, V. J. (2015). The interplay of game elements with psychometric qualities, learning, and enjoyment in game-based assessment. Computers & Education, 87, 340–356. https://doi.org/10.1016/j.compedu.2015.07.009CrossRef

Kotsopoulos, D., Floyd, L., Khan, S., Namukasa, I. K., Somanath, S., Weber, J., & Yiu, C. (2017). A pedagogical framework for computational thinking. Digital Experiences in Mathematics Education, 3(2), 154–171. https://doi.org/10.1007/s40751-017-0031-2CrossRef

Kusnendar, J., & Prabawa, H. (2019). Bajo’s Adventure: An effort to develop students computational thinking skills through mobile application. Journal of Physics: Conference Series.

Lockwood, J., & Mooney, A. (2018). Computational thinking in education: Where does it fit? A systematic literary review. International Journal of Computer Science Education in Schools, 2(1), 41. https://doi.org/10.21585/ijcses.v2i1.26CrossRef

Luo, F., Antonenko, P. D., & Davis, E. C. (2020). Exploring the evolution of two girls’ conceptions and practices in computational thinking in science. Computers & Education, 146, 103759. https://doi.org/10.1016/j.compedu.2019.103759

Majer, J. M. (2009). Self-efficacy and academic success among ethnically diverse first-generation community college students. Journal of Diversity in Higher Education, 2(4), 243. https://doi.org/10.1037/a0017852CrossRef

Manske, S., Werneburg, S., & Hoppe, H. U. (2019). Learner Modeling and Learning Analytics in Computational Thinking Games for Education. In Data Analytics Approaches in Educational Games and Gamification Systems (pp. 187–212). Springer.

Meerbaum-Salant, O., Armoni, M., & Ben-Ari, M. (2011). Habits of programming in scratch. In Proceedings of the 16th annual joint conference on Innovation and technology in computer science education.

Menard, S. (2001). Collinearity. Applied logistic regression analysis Second Edition (Quantitative applications in the social sciences), (pp. 75–78) Sage Publications, Inc.

Min, S., So, K. K. F., & Jeong, M. (2019). Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model. Journal of Travel & Tourism Marketing, 36(7), 770–783. https://doi.org/10.1080/10548408.2018.1507866CrossRef

Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Integrating acceptance and motivational factors into a combined model of Self-Determination Theory and Technology Acceptance. Computers in Human Behavior, 68, 83–95. https://doi.org/10.1016/j.chb.2016.11.020CrossRef

Nunnally, J. C. (1994). Psychometric theory. Tata McGraw-hill education.

Papert, S. A. (1980). Mindstorms: Children, computers, and powerful ideas. Basic books.

Pila, S., Aladé, F., Sheehan, K. J., Lauricella, A. R., & Wartella, E. A. (2019). Learning to code via tablet applications: An evaluation of Daisy the Dinosaur and Kodable as learning tools for young children. Computers & Education, 128, 52–62. https://doi.org/10.1016/j.compedu.2018.09.006CrossRef

Pituch, K. A., & Lee, Y.-K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222–244. https://doi.org/10.1016/j.compedu.2004.10.007CrossRef

Rose, S. (2016). Bricolage Programming and Problem Solving Ability in Young Children: An Exploratory Study. European Conference on Games Based Learning, 914.

Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020CrossRef

Ryan, R. M., Rigby, C. S., & Przybylski, A. (2006). The motivational pull of video games: A self-determination theory approach. Motivation and Emotion, 30(4), 344–360. https://doi.org/10.1007/s11031-006-9051-8CrossRef

Segers, M. S. (1997). An alternative for assessing problem-solving skills: The overall test. Studies in Educational Evaluation, 23(4), 373–398. https://doi.org/10.1016/S0191-491X(97)86216-5CrossRef

Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. https://doi.org/10.1016/j.edurev.2017.09.003CrossRef

Steinmayr, R., & Spinath, B. (2009). The importance of motivation as a predictor of school achievement. Learning and Individual Differences, 19(1), 80–90. https://doi.org/10.1016/j.lindif.2008.05.004CrossRef

Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (Vol. 5). Boston, MA: Pearson.

Theodoropoulos, A., Antoniou, A., & Lepouras, G. (2017). How do different cognitive styles affect learning programming? Insights from a game-based approach in Greek schools. ACM Transactions on Computing Education (TOCE), 17(1), 3. https://doi.org/10.1145/2940330CrossRef

Tohidi, H., & Jabbari, M. M. (2012). The effects of motivation in education. Procedia-Social and Behavioral Sciences, 31, 820-824. CrossRef

Werneburg, S., Manske, S., Feldkamp, J., & Hoppe, H. U. (2018). Improving on guidance in a gaming environment to foster computational thinking. In Proceedings of the 26th International Conference on Computers in Education.

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215.CrossRef

Wu, B., Hu, Y., Ruis, A. R., & Wang, M. (2019). Analysing computational thinking in collaborative programming: A quantitative ethnography approach. Journal of Computer Assisted Learning, 35(3), 421–434. https://doi.org/10.1111/jcal.12348CrossRef

Xu, F. & Correia, A. (2021). A systematic review of distributed pair programming based on the team effectiveness model. In Proceedings of Fifth APSCE International Conference on Computational Thinking and STEM Education 2021 (CTE-STEM).

Yeni, S., & Cagiltay, K. (2017). A heuristic evaluation to support the instructional and enjoyment aspects of a math game. Program, 51(4). https://doi.org/10.1108/PROG-07-2016-0050

Yücel, Y., & Rızvanoğlu, K. (2019). Battling gender stereotypes: A user study of a code-learning game, “Code Combat”, with middle school children. Computers in Human Behavior, 99, 352–365. https://doi.org/10.1016/j.chb.2019.05.029CrossRef

Zhang, S., Wong, K. W. G., Chan, C. F. P. (2021). Achievement and effort in acquiring computational thinking concepts: a log-based analysis in a game-based learning environment. In Proceedings of Fifth APSCE International Conference on Computational Thinking and STEM Education 2021 (CTE-STEM).

Zhao, W., & Shute, V. J. (2019). Can playing a video game foster computational thinking skills? Computers & Education, 141, 103633. https://doi.org/10.1016/j.compedu.2019.103633.CrossRef

Zhi, R., Lytle, N., & Price, T. W. (2018). Exploring instructional support design in an educational game for K-12 computing education. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education. https://doi.org/10.1145/3159450.3159519

Titel: Playing coding games to learn computational thinking: What motivates students to use this tool at home?
verfasst von: Shuhan Zhang
Gary K. W. Wong
Peter C. F. Chan
Publikationsdatum: 27.06.2022
Verlag: Springer US
Erschienen in: Education and Information Technologies / Ausgabe 1/2023
Print ISSN: 1360-2357
Elektronische ISSN: 1573-7608
DOI: https://doi.org/10.1007/s10639-022-11181-7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 1/2023

Measuring the effect of social media on student academic performance using a social media influence factor model

Framework for the adaptation of an autonomous academic recommendation system as a service-oriented architecture

A phenomenographic approach on teacher conceptions of teaching Artificial Intelligence (AI) in K-12 schools

Does a flipped classroom model work in mathematics education? A meta-analysis

Artificial intelligence inspired multilanguage framework for note-taking and qualitative content-based analysis of lectures

Indian government initiatives on cyberbullying: A case study on cyberbullying in Indian higher education institutions

Premium Partner