The weak link between classroom gamification and scientific literacy: A multi-method correlational study of senior high school students in a green chemistry context
Dewi Satria Ahmar 1 * , Muhammad Fath Azzajjad 2, Tri Santoso 1, Muhammad Ismail 3
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1 Department of Chemistry Education, Faculty of Teacher Training and Education, Tadulako University, Indonesia
2 Department of Chemistry Education, Faculty of Teacher Training and Education, Universitas Sembilanbelas November Kolaka, Indonesia
3 Department of Sports Coaching Education, Faculty of Teacher Training and Education, Tadulako University, Indonesia
* Corresponding Author

Abstract

Gamification, the use of game design elements such as points, badges, and competition in classroom instruction, is widely promoted as a strategy to raise student motivation and, by extension, learning outcomes, yet empirical evidence for its downstream effect on measured achievement remains mixed. This study examined the relationship between classroom gamification and scientific literacy among senior high school students across eight schools in Palu, Central Sulawesi, Indonesia, using a durian-waste, green-chemistry context as the literacy assessment theme. A gamification questionnaire and a three-tier diagnostic scientific literacy test were administered to the same students, and four complementary analytical strategies, exploratory factor analysis, cluster analysis, correlation and network analysis, and structural equation modeling, were applied to examine whether gamification experience predicts scientific literacy achievement and misconception profile, and whether this relationship depends on the school a student attends. The gamification instrument proved to be empirically closer to a single general engagement construct than to the four-dimension structure assumed at the outset, and one of the four hypothesized subscales did not meet conventional reliability standards. Across all four analytical strategies, gamification experience showed no reliable association with scientific literacy achievement, whether examined through total score, engagement-based student groupings, item-level network structure, or a latent structural model; a school-level moderation test likewise found no evidence that this null relationship differed across schools. Gamification exposure, engagement, and enjoyment, as reported by students, were therefore not, by themselves, sufficient predictors of scientific literacy achievement in this setting, and any relationship that does exist more plausibly operates through how consistently gamified instruction is implemented in the classroom than through student-reported experience alone. Implications for context-based, green-chemistry science instruction, and for the measurement of both classroom gamification and scientific literacy in similar settings, are discussed.

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References

  • Aiken, L. R. (1985). Three coefficients for analyzing the reliability and validity of ratings. Educational and Psychological Measurement, 45(1), 131-142. https://doi.org/10.1177/0013164485451012
  • Aikenhead, G. S. (2006). Science education for everyday life: Evidence-based practice. Teachers College Press.
  • Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. https://doi.org/10.1037/0033-2909.103.3.411
  • Arifin, Z., Sukarmin, S., & Saputro, S. (2025). Trends and research frontiers in socioscientific issues for sustainable science education: A systematic and bibliometric analysis from 2014–2024. Journal of Pedagogical Research, 9(1), 407-434. https://doi.org/10.33902/JPR.202530575
  • Bilgin, I., Uzuntiryaki, E., & Geban, O. (2003). Students' misconceptions on the concept of chemical equilibrium. Journal of Education and Science, 28(127), 10-15.
  • Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16(1), 5-13. https://doi.org/10.1002/wps.20375
  • Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge.
  • Caleon, I., & Subramaniam, R. (2010). Development and application of a three-tier diagnostic test to assess secondary students' understanding of waves. International Journal of Science Education, 32(7), 939-961. https://doi.org/10.1080/09500690902890130
  • Chandrasegaran, A. L., Treagust, D. F., & Mocerino, M. (2007). The development of a two-tier multiple-choice diagnostic instrument for evaluating secondary school students' ability to describe and explain chemical reactions using multiple levels of representation. Chemistry Education Research and Practice, 8(3), 293-307. https://doi.org/10.1039/B7RP90006F
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  • Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1-9.
  • Çelik Demirci, S., Kul, Ü., & Sevimli, E. (2023). Turkish adaptation of the Mathematics Teachers' Beliefs Scale. Journal of Pedagogical Sociology and Psychology, 5(2), 92–104. https://doi.org/10.33902/jpsp.202323416
  • Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627-668. https://doi.org/10.1037/0033-2909.125.6.627
  • Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum.
  • Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness: Defining "gamification". In A. Lugmayr, H. Franssila, C. Safran, & I. Hammouda (Eds.), Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments (pp. 9-15). ACM. https://doi.org/10.1145/2181037.2181040
  • Dichev, C., & Dicheva, D. (2017). Gamifying education: What is known, what is believed and what remains uncertain: A critical review. International Journal of Educational Technology in Higher Education, 14, 9. https://doi.org/10.1186/s41239-017-0042-5
  • Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297-316. https://doi.org/10.25300/MISQ/2015/39.2.02
  • Do, T. H., Nguyen, N. P. N., Duong, T. T. A., Nguyen, D. V., & Vuong, T. X. (2025). Durian peel-seed biochar for efficient methylene blue removal from water: Synthesis, characterization, and adsorption performance. RSC Advances, 15(40), 33726-33749. https://doi.org/10.1039/d5ra05313g
  • Dorsah, P., Amedeker, M. K., & Ngman-Wara, E. I. (2025). Exploring the factor structure of the Epistemic Beliefs Inventory. International Journal of Didactical Studies, 6(2), 33004. https://doi.org/10.33902/ijods.202533004
  • Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195-212. https://doi.org/10.3758/s13428-017-0862-1
  • Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster analysis (5th ed.). Wiley.
  • Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299. https://doi.org/10.1037/1082-989X.4.3.272
  • Fadillah, M. A., Usmeldi, U., Lufri, L., Mawardi, M., & Festiyed, F. (2025). Systematic literature review of inquiry-based learning models for optimizing learning outcomes at various educational levels. Journal of Pedagogical Sociology and Psychology, 7(4), 278-293. https://doi.org/10.33902/jpsp.202532540
  • Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice Hall.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.
  • Hamari, J., Koivisto, J., & Sarsa, H. (2014). Does gamification work? A literature review of empirical studies on gamification. In R. H. Sprague Jr. (Ed.), Proceedings of the 47th Hawaii International Conference on System Sciences (pp. 3025-3034). IEEE. https://doi.org/10.1109/HICSS.2014.377
  • Hasan, S., Bagayoko, D., & Kelley, E. L. (1999). Misconceptions and the certainty of response index (CRI). Physics Education, 34(5), 294-299. https://doi.org/10.1088/0031-9120/34/5/304
  • Hellberg, A.-S. (2023). The story of the hatter and the agile methods course: Gamification and game thinking in education. Journal of Pedagogical Research, 7(3), 19-42. https://doi.org/10.33902/JPR.202320130
  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  • Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36. https://doi.org/10.1007/BF02291575
  • Kalogiannakis, M., Papadakis, S., & Zourmpakis, A.-I. (2021). Gamification in science education: A systematic review of the literature. Education Sciences, 11(1), 22. https://doi.org/10.3390/educsci11010022
  • Kapp, K. M. (2012). The gamification of learning and instruction: Game-based methods and strategies for training and education. Pfeiffer.
  • Kirbulut, Z. D., & Geban, O. (2014). Using three-tier diagnostic test to assess students' misconceptions of states of matter. Eurasia Journal of Mathematics, Science and Technology Education, 10(5), 509-521. https://doi.org/10.12973/eurasia.2014.1128a
  • Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
  • Nunez-Pacheco, R., Vidal, E., Castro-Gutierrez, E., Turpo-Gebera, O., Barreda-Parra, A., & Aguaded, I. (2023). Use of a gamified platform to improve scientific writing in engineering students. Education Sciences, 13(12), 1164. https://doi.org/10.3390/educsci13121164
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
  • OECD. (2006). Assessing scientific, reading and mathematical literacy: A framework for PISA 2006. OECD Publishing. https://doi.org/10.1787/9789264026407-en
  • OECD. (2023). PISA 2025 science framework (second draft). OECD Publishing.
  • Okariz, A., Huebra, M., Sarasola, A., Ibarretxe, J., Bidegain, G., & Zubimendi, J. L. (2023). Gamifying physics laboratory work increases motivation and enhances acquisition of the skills required for application of the scientific method. Education Sciences, 13(3), 302. https://doi.org/10.3390/educsci13030302
  • Ratinho, E., & Martins, C. (2023). The role of gamified learning strategies in student's motivation in high school and higher education: A systematic review. Heliyon, 9(8), e19033. https://doi.org/10.1016/j.heliyon.2023.e19033
  • Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20(1), 53-65. https://doi.org/10.1016/0377-0427(87)90125-7
  • Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78. https://doi.org/10.1037/0003-066X.55.1.68
  • Sailer, M., Hense, J. U., Mayr, S. K., & Mandl, H. (2017). How gamification motivates: An experimental study of the effects of specific game design elements on psychological need satisfaction. Computers in Human Behavior, 69, 371-380. https://doi.org/10.1016/j.chb.2016.12.033
  • Seaborn, K., & Fels, D. I. (2015). Gamification in theory and action: A survey. International Journal of Human-Computer Studies, 74, 14-31. https://doi.org/10.1016/j.ijhcs.2014.09.006
  • Sheppard, K. (2006). High school students' understanding of titrations and related acid-base phenomena. Chemistry Education Research and Practice, 7(1), 32-45. https://doi.org/10.1039/B5RP90014J
  • Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322-2347. https://doi.org/10.1108/EJM-02-2019-0189
  • Sökmen, Y., Aygün, E. B., & Ateş Köysu, N. (2026). Integrating augmented reality, virtual reality, and artificial intelligence into game-based learning: A systematic review. Journal of Pedagogical Sociology and Psychology, 8(2), e44630. https://doi.org/10.33902/JPSP.202644630
  • Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2019). Using multivariate statistics (7th ed.). Pearson.
  • Taber, K. S. (2002). Chemical misconceptions: Prevention, diagnosis and cure (Vol. 1). Royal Society of Chemistry.
  • Treagust, D. F. (1988). Development and use of diagnostic tests to evaluate students' misconceptions in science. International Journal of Science Education, 10(2), 159-169. https://doi.org/10.1080/0950069880100204
  • Vrabec, M., & Proksa, M. (2016). Identifying misconceptions related to chemical bonding concepts in the Slovak school system using the bonding representations inventory as a diagnostic tool. Journal of Chemical Education, 93(8), 1364-1370. https://doi.org/10.1021/acs.jchemed.5b00953

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