Acceptance of Game-Based Learning and Intrinsic Motivation as Predictors for Learning Success and Flow Experience

Manuel Ninaus, Korbinian Moeller, Jake McMullen, Kristian Kiili


There is accumulating evidence that engagement with digital math games can improve students’ learning. However, in what way individual variables critical to game-based learning influence students' learning success still needs to be explored. Therefore, the aim of this study was to investigate the influence of students’ acceptance of game-based learning (e.g., perceived usefulness of a game as a learning tool, perceived ease of use), as well as their intrinsic motivation for math (e.g., their math interest, self-efficacy) and quality of playing experience on learning success in a game-based rational number training. Additionally, we investigated the influence of the former variables on quality of playing experience (operationalized as perceived flow). Results indicated that the game-based training was effective. Moreover, students’ learning success and their quality of playing experience were predicted by measures of acceptance of game-based learning and intrinsic motivation for math. These findings indicated that learning success in game-based learning approaches are driven by students’ acceptance of the game as a learning tool and content-specific intrinsic motivation. Therefore, the present work is of particular interest to researchers, developers, and practitioners working with game-based learning environments.


technology acceptance; intrinsic motivation; user experience; math game; rational numbers; game-based learning

Full Text:



Hainey, T., Connolly, T. M., Boyle, E. A., Wilson, A., & Razak, A., “A systematic literature review of games-based learning empirical evidence in primary education,” Computers & Education, Vol. 102, pp. 202–223, November.2016.

Kiili, K., Devlin, K., & Multisilta, J., “Editorial : Is game-based math learning finally coming of age ?,” International Journal of Serious Games, Vol. 2, No. 4, pp. 3–6, 2015.

Ninaus, M., Kiili, K., McMullen, J., & Moeller, K., “Assessing fraction knowledge by a digital game,” Computers in Human Behavior, Vol. 70, pp. 197–206, May.2017.

Kiili, K. & Ketamo, H., “Evaluating Cognitive and Affective Outcomes of a Digital Game-Based Math Test,” IEEE Transactions on Learning Technologies, 2017.

Li, Q. & Ma, X., “A meta-analysis of the effects of computer technology on school students’ mathematics learning,” Educational Psychology Review, Vol. 22, No. 3, pp. 215–243, 2010.

Cohen Kadosh, R., Dowker, A., Heine, A., Kaufmann, L., & Kucian, K., “Interventions for improving numerical abilities: Present and future,” Trends in Neuroscience and Education, Vol. 2, No. 2, pp. 85–93, 2013.

Käser, T. et al., “Design and evaluation of the computer-based training program Calcularis for enhancing numerical cognition,” Frontiers in Psychology, Vol. 4, No. AUG, pp. 1–13, 2013.

Wilson, A. J., Dehaene, S., Dubois, O., & Fayol, M., “Effects of an adaptive game intervention on accessing number sense in low-socioeconomic-status kindergarten children,” Mind, Brain, and Education, Vol. 3, No. 4, pp. 224–234, 2009.

Shin, N., Sutherland, L. M., Norris, C. A., & Soloway, E., “Effects of game technology on elementary student learning in mathematics,” British Journal of Educational Technology, Vol. 43, No. 4, pp. 540–560, 2012.

Ke, F., “A case study of computer gaming for math: Engaged learning from gameplay?,” Computers & Education, Vol. 51, No. 4, pp. 1609–1620, December.2008.

Booth, J. L. & Siegler, R. S., “Developmental and individual differences in pure numerical estimation.,” Developmental psychology, Vol. 42, No. 1, pp. 189–201, 2006.

Fazio, L. K., Kennedy, C. A., & Siegler, R. S., “Improving Children’s Knowledge of Fraction Magnitudes,” Plos One, Vol. 11, No. 10, p. e0165243, 2016.

Schneider, M. & Stern, E., “The developmental relations between conceptual and procedural knowledge: a multimethod approach.,” Developmental psychology, Vol. 46, No. 1, pp. 178–192, 2010.

Link, T., Nuerk, H.-C., & Moeller, K., “On the relation between the mental number line and arithmetic competencies,” The quarterly journal of experimental psychology, Vol. 67, No. 8, pp. 1597–1613, 2014.

Siegler, R. S., Fazio, L. K., Bailey, D. H., & Zhou, X., “Fractions: The new frontier for theories of numerical development,” Trends in Cognitive Sciences, Vol. 17, No. 1, pp. 13–19, 2013.

Chen, Z. H., Liao, C. C. Y., Cheng, H. N. H., Yeh, C. Y. C., & Chan, T. W., “Influence of game quests on pupils’ enjoyment and goal-pursuing in math learning,” Educational Technology and Society, Vol. 15, No. 2, pp. 317–327, 2012.

Lumsden, J., Skinner, A., Woods, A. T., Lawrence, N. S., & Munafò, M., “The effects of gamelike features and test location on cognitive test performance and participant enjoyment,” PeerJ, Vol. 4, p. e2184, 2016.

Hamari, J., Koivisto, J., & Sarsa, H., “Does gamification work? - A literature review of empirical studies on gamification,” Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 3025–3034, 2014.

Ryan, R. M. & Deci, E. L., “Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions.,” Contemporary educational psychology, Vol. 25, No. 1, pp. 54–67, 2000.

Deci, E. L. & Ryan, R. M., “The ‘ What ’ and ‘ Why ’ of Goal Pursuits: Human Needs and the Self-Determination of Behavior,” Psychological Inquiry, Vol. 11, No. 4, pp. 227–268, 2000.

Skaalvik, E. M., Federici, R. A., & Klassen, R. M., “Mathematics achievement and self-efficacy: Relations with motivation for mathematics,” International Journal of Educational Research, Vol. 72, pp. 129–136, 2015.

Peters, M. L., “Examining the relationships among classroom climate, self-efficacy, and achievement in undergraduate mathematics: A multi-level analysis,” International Journal of Science and Mathematics Education, Vol. 11, No. 2, pp. 459–480, 2013.

Reyes, L. H., “Affective Variables and Mathematics Education,” The Elementary School Journal, Vol. 84, No. 5, pp. 558–581, 1984.

Campbell, N. K. & Hackett, G., “The effects of mathematics task performance on math self-efficacy and task interest,” Journal of Vocational Behavior, Vol. 28, No. 2, pp. 149–162, 1986.

Pajares, F. & Miller, M. D., “Role of self-efficacy and self-concept beliefs in mathematical problem solving: A path analysis,” Journal of Educational Psychology, Vol. 86, No. 2, pp. 193–203, 1994.

Boyle, E. A. et al., “An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games,” Computers & Education, Vol. 94, pp. 178–192, March.2016.

Kenny, R. F. & McDaniel, R., “The role teachers’ expectations and value assessments of video games play in their adopting and integrating them into their classrooms,” British Journal of Educational Technology, Vol. 42, No. 2, pp. 197–213, 2011.

Sandford, R., Ulicsak, M., Facer, K., & Rudd, T., Teaching with games: Using commercial off-the-shelf computer games in formal education, Vol. 112. Bristol: Futurelab, 2006.

Bourgonjon, J., De Grove, F., De Smet, C., Van Looy, J., Soetaert, R., & Valcke, M., “Acceptance of game-based learning by secondary school teachers,” Computers & Education, Vol. 67, pp. 21–35, September.2013.

Bourgonjon, J., Valcke, M., Soetaert, R., De Wever, B., & Schellens, T., “Parental acceptance of digital game-based learning,” Computers and Education, Vol. 57, No. 1, pp. 1434–1444, 2011.

Rieber, L. P., “Seriously considering play: Designing interactive learning environments based on the blending of microworlds, simulations, and games,” Educational Technology Research and Development, Vol. 44, No. 2, pp. 43–58, 1996.

Bourgonjon, J., Valcke, M., Soetaert, R., & Schellens, T., “Students’ perceptions about the use of video games in the classroom,” Computers & Education, Vol. 54, No. 4, pp. 1145–1156, May.2010.

Davis, F. D., “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly, Vol. 13, No. 3, p. 319, September.1989.

Korostenskaja, M. et al., “Real-time functional mapping with electrocorticography in pediatric epilepsy: comparison with fMRI and ESM findings.,” Clinical EEG and neuroscience, Vol. 45, No. 3, pp. 205–11, July.2014.

Lee, Y.-H. H., Hsieh, Y.-C. C., & Chen, Y.-H. H., “An investigation of employees’ use of e-learning systems: Applying the Technology Acceptance Model,” Behaviour & Information Technology, Vol. 32, No. 2, pp. 173–189, 2013.

Lee, Y. H., Hsiao, C., & Purnomo, S. H., “An empirical examination of individual and system characteristics on enhancing e-learning acceptance,” Australasian Journal of Educational Technology, Vol. 30, No. 5, pp. 562–579, 2014.

Shen, C. C. & Chuang, H. M., “Exploring users’ attitudes and intentions toward the interactive whiteboard technology environment,” International Review on Computers and Software, Vol. 5, No. 2, pp. 200–208, 2010.

Procci, K., Singer, A. R., Levy, K. R., & Bowers, C., “Measuring the flow experience of gamers: An evaluation of the DFS-2,” Computers in Human Behavior, Vol. 28, pp. 2306–2312, 2012.

Kiili, K., Lainema, T., de Freitas, S., & Arnab, S., “Flow framework for analyzing the quality of educational games,” Entertainment Computing, Vol. 5, No. 4, pp. 367–377, 2014.

Csikszentmihalyi, M., Beyond boredom and anxiety. Jossey-Bass Publishers, 2000.

Perttula, A., Kiili, K., Tuomi, P., & Lindstedt, A., “Flow experience in game based learning – a systematic literature review,” International Journal of Serious Games, Vol 4,1, 2017, pp. 57-72.

Pekrun, R., Goetz, T., Titz, W., & Perry, R. P., Positive Emotions in Education, in Beyond Coping, Oxford University Press, 2002, pp. 149–174.

Engeser, S. & Rheinberg, F., “Flow, performance and moderators of challenge-skill balance,” Motivation and Emotion, Vol. 32, No. 3, pp. 158–172, September.2008.

Csikszentmihalyi, M., Flow: The Psychology of Optimal Experience, 2nd ed. New York: Harper & Row, 2002.

Ninaus, M., Pereira, G., Stefitz, R., Prada, R., Paiva, A., & Wood, G., “Game elements improve performance in a working memory training task,” International Journal of Serious Games, Vol. 2, No. 1, pp. 3–16, 2015.

Wouters, P., van Nimwegen, C., van Oostendorp, H., & van der Spek, E. D., “A meta-analysis of the cognitive and motivational effects of serious games.,” Journal of Educational Psychology, Vol. 105, No. 2, pp. 249–265, 2013.

Lumsden, J., Edwards, E. A., Lawrence, N. S., Coyle, D., & Munafò, M. R., “Gamification of Cognitive Assessment and Cognitive Training: A Systematic Review of Applications and Efficacy,” JMIR Serious Games, Vol. 4, No. 2, p. e11, 2016.

National Mathematics Advisory Panel, “Foundations for success: The final report of the National Mathematics Advisory Panel,” Washington D.C., 2008.

Bailey, D. H., Hoard, M. K., Nugent, L., & Geary, D. C., “Competence with fractions predicts gains in mathematics achievement,” Journal of Experimental Child Psychology, Vol. 113, No. 3, pp. 447–455, 2012.

Booth, J. L. & Newton, K. J., “Fractions: Could they really be the gatekeeper’s doorman?,” Contemporary Educational Psychology, Vol. 37, No. 4, pp. 247–253, 2012.

Ninaus, M., Kiili, K., McMullen, J., & Moeller, K., “A Game-Based Approach to Examining Students’ Conceptual Knowledge of Fractions,” in Proceedings of Games and Learning Alliance conference (GALA 2016) - Lecture Notes in Computer Science, 2016, Vol. 10056, pp. 37–49,

Berger, J. L. & Karabenick, S. A., “Motivation and students’ use of learning strategies: Evidence of unidirectional effects in mathematics classrooms,” Learning and Instruction, Vol. 21, No. 3, pp. 416–428, 2011.

Rheinberg, F., Vollmeyer, R., & Engeser, S., Die Erfassung des Flow-Erlebens [measuring flow-experience], in Diagnostik von Motivation und Selbstkonzept, Göttingen: Hogrefe, 2003, pp. 261–279.

R Core Team, “R: A language and environment for statistical computing. R Foundation for Statistical Computing.” Vienna, Austria, 2016.

Wei, T. & Simko, V., “corrplot: Visualization of a Correlation Matrix. R package version 0.77.” 2016.

Cohen, J., Statistical Power Analysis for the Behavioral Sciences, 2nd ed. New York, New York, USA: Lawrence Erlbaum Associates, 1988.

Godden, D. R. & Baddeley, A., “Context-Dependent Memory in Two Natural Environments: On Land and Underwater,” British Journal of Psychology, Vol. 66, No. 3, pp. 325–331, August.1975.

Kettanurak, V., Ramamurthy, K., & Haseman, W. D., “User attitude as a mediator of learning performance improvement in an interactive multimedia environment: an empirical investigation of the degree of interactivity and learning styles,” International Journal of Human-Computer Studies, Vol. 54, pp. 541–583, 2001.

Igbaria, M. & Tan, M., “The consequences of information technology acceptance on subsequent individual performance,” Information & Management, Vol. 32, No. 3, pp. 113–121, 1997.

Chu, R. J. & Chu, A. Z., “Multi-level analysis of peer support, Internet self-efficacy and e-learning outcomes - The contextual effects of collectivism and group potency,” Computers and Education, Vol. 55, No. 1, pp. 145–154, 2010.

Liaw, S. S., “Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system,” Computers and Education, Vol. 51, No. 2, pp. 864–873, 2008.

Torkzadeh, G., Chang, J. C. J., & Demirhan, D., “A contingency model of computer and Internet self-efficacy,” Information & Management, Vol. 43, No. 4, pp. 541–550, 2006.

Liaw, S. S. & Huang, H. M., “Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments,” Computers and Education, Vol. 60, No. 1, pp. 14–24, 2013.

Sharma, S., Dick, G., Chin, W., & Land, L., “Self-Regulation and E-Learning,” in ECIS 2007 Proceedings, 2007.

Garris, R., Ahlers, R., & Driskell, J. E., “Games, Motivation, and Learning: A Research and Practice Model,” Simulation & Gaming, Vol. 33, No. 4, pp. 441–467, December.2002.

Keller, J. & Bless, H., “Flow and regulatory compatibility: an experimental approach to the flow model of intrinsic motivation.,” Personality & social psychology bulletin, Vol. 34, No. 2, pp. 196–209, February.2008.

Hwang, G.-J., Wu, P.-H., & Chen, C.-C., “An online game approach for improving students’ learning performance in web-based problem-solving activities,” Computers & Education, Vol. 59, No. 4, pp. 1246–1256, 2012.

Hsu, C.-L. & Lu, H.-P., “Why do people play on-line games? An extended TAM with social influences and flow experience,” Information & Management, Vol. 41, No. 7, pp. 853–868, September.2004.

Ghani, J. A., Flow in human-computer interactions: test of a model, in Human factors in management information systems: emerging theoretical bases, J. Carey, Ed. Norwood, NJ: Ablex Publishing Corp, 1995, pp. 291–311.

Rodriguez-Sanchez, A. M., Schaufeli, W. B., Salanova, M., & Cifre, E., “Flow experience among information and communication technology users,” Psychological Reports, Vol. 102, pp. 29–39, 2008.

Liu, S. & Yuan, C., “Applying The Technology Acceptance Model and Flow Theory to Online E-Learning Users’ Acceptance Behavior,” International Association for Computer Information Systems, Vol. VI, No. 2, pp. 175–181, 2005.

Tang, J. E. & Chiang, C., “Towards an Understanding of the Behavioral Intention to Use Mobile Knowledge Management,” WSEAS Transactions on Information Science and Application, Vol. 6, No. 9, pp. 1601–1613, 2009.

Moon, J. W. & Kim, Y. G., “Extending the TAM for a World-Wide-Web context,” Information and Management, Vol. 38, No. 4, pp. 217–230, 2001.

Chen, H., Wigand, R. T., & Nilan, M., “Exploring Web users’ optimal flow experiences,” Information Technology & People, Vol. 13, No. 4, pp. 263–281, 2000.

Wang, L. & Chen, M., “The effects of game strategy and preference‐matching on flow experience and programming performance in game‐based learning,” Innovations in Education and Teaching International, Vol. 47, No. 1, pp. 39–52, February.2010.

Lent, R. W., Brown, S. D., & Larkin, K. C., “Self-efficacy in the

prediction of academic performance and perceived career options.,” Journal of Counseling Psychology, Vol. 33, No. 3, pp. 265–269, 1986.

Lent, R. W., Brown, S. D., & Hackett, G., “Toward a Unifying Social Cognitive Theory of Career and Academic Interest, Choice, and Performance,” Journal of Vocational Behavior, Vol. 45, No. 1, pp. 79–122, 1994.

Silvia, P. J., “Self-efficacy and interest: Experimental studies of optimal incompetence,” Journal of Vocational Behavior, Vol. 62, No. 2, pp. 237–249, 2003.

Wiebe, E. N., Lamb, A., Hardy, M., & Sharek, D., “Measuring engagement in video game-based environments: Investigation of the User Engagement Scale,” Computers in Human Behavior, Vol. 32, pp. 123–132, March.2014.

Fang, X. & Zhao, F., “Personality and enjoyment of computer game play,” Computers in Industry, Vol. 61, No. 4, pp. 342–349, 2010.

Procci, K., Bohnsack, J., & Bowers, C., Patterns of Gaming Preferences and Serious Game Effectiveness, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 6774, No. PART 2, R. Shumaker, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 37–43.

Ibrahim, R. & Jaafar, A., “User Acceptance of Educational Games: A revised unified theory of acceptance and use of technology,” International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, Vol. 5, No. 5, pp. 557–563, 2011.

Kalyuga, S., “Expertise reversal effect and its implications for learner-tailored instruction,” Educational Psychology Review, Vol. 19, No. 4, pp. 509–539, 2007.



  • There are currently no refbacks.

Serious Games Society

Creative Commons LicenseThe International Journal of Serious Games (IJSG) by Serious Games Society is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.