What Are the Factors Influencing Science Learning in the Discovery Model? An Exploration of Issues to Create Innovation
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Abstract
The issues in science learning are not solely related to individual factors but involve interacting factors, where both internal factors, such as self-efficacy, motivation, epistemological beliefs, and curiosity, as well as external factors such as learning media and technology readiness, dynamically interact to shape students’ perceptions and impact their learning. This study identifies and analyzes the factors influencing school science teaching, focusing on developing innovative strategies to address existing challenges. The research method employed is a mixed-method approach, utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis to test hypotheses and in-depth interviews to explore the challenges, issues, and expectations of science learning. The findings indicate that curiosity significantly impacts engagement in learning (p-value = 0.003) and learning models (p-value = 0.002), suggesting that students’ curiosity enhances their engagement in learning and influences the selection of learning models. Motivation significantly affects learning models (p-value = 0.011), not engagement or media usage. Furthermore, technology readiness plays a significant role in engagement in learning (p-value = 0.002) and learning media (p-value = 0.000), but does not influence the learning model choice. Interviews with teachers also revealed that the primary challenge is providing appropriate media to stimulate students, particularly for challenging topics, and the need for more interactive and real-world problem-based media to support discovery learning more effectively.
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References
Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183–198. https://doi.org/10.1016/j.learninstruc.2006.03.001
Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
Barenthien, J. M., & Dunekacke, S. (2022). The implementation of early science education in preschool teachers’ initial teacher education. A survey of teacher educators about their aims, practices and challenges in teaching science. Journal of Early Childhood Teacher Education, 43(4), 600–618. https://doi.org/10.1080/10901027.2021.1962443
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Britner, S. L., & Pajares, F. (2006). Sources of science self-efficacy beliefs of middle school students. Journal of Research in Science Teaching, 43(5), 485–499. https://doi.org/10.1002/tea.20131
Bruner, J. S. (1961). The act of discovery. Harvard Educational Review, 32, 21–32.
Chen, C.-H., Huang, C.-Y., & Chou, Y.-Y. (2019). Effects of augmented reality-based multidimensional concept maps on students’ learning achievement, motivation and acceptance. Universal Access in the Information Society, 18(2), 257–268. https://doi.org/10.1007/s10209-017-0595-z
Chin, W. W. (1998). he partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research. Lawrence Erlbaum Associates Publishers.
Cho, H. J., Zhao, K., Lee, C. R., Runshe, D., & Krousgrill, C. (2021). Active learning through flipped classroom in mechanical engineering: improving students’ perception of learning and performance. International Journal of STEM Education, 8(1), 46. https://doi.org/10.1186/s40594-021-00302-2
Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences. Routledge. https://doi.org/10.4324/9780203771587
Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage.
de Jong, T., Sotiriou, S., & Gillet, D. (2014). Innovations in STEM education: the Go-Lab federation of online labs. Smart Learning Environments, 1(1), 3. https://doi.org/10.1186/s40561-014-0003-6
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
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y
Eccles, J. S., & Wigfield, A. (2002). Motivational Beliefs, Values, and Goals. Annual Review of Psychology, 53(1), 109–132. https://doi.org/10.1146/annurev.psych.53.100901.135153
Firdaus, T. (2025). The Philosophical Construction of Educational Science in Relation to Posthumanism and Transhumanism in Artificial Intelligence. Turkish Academic Research Review - Türk Akademik Araştırmalar Dergisi [TARR], 10(1), 70–83. https://doi.org/10.30622/tarr.1610935
Firdaus, T., Amelia, A., Alifiyah, F. L. N., Nahdliyah, A. S., & Fausiyeh, F. (2025). Trends and Effects of Psychological and Cognitive Load in Education. Journal of Education and Learning Reviews, 2(2), 97–128. https://doi.org/10.60027/jelr.2025.1084
Firdaus, T., Nurohman, S., Wilujeng, I., & Rahmawati, L. (2025). Exploring Motivational, Cognitive, and Instructional of Critical Thinking Disposition in Science Learning: The Mediating Role of Student Self-Regulation. International Journal of Science Education and Teaching, 4(1), 12–25. https://doi.org/10.14456/ijset.2025.02
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
Geisser, S. (1975). The Predictive Sample Reuse Method with Applications. Journal of the American Statistical Association, 70(350), 320. https://doi.org/10.2307/2285815
Haatainen, O., Turkka, J., & Aksela, M. (2021). Science Teachers’ Perceptions and Self-Efficacy Beliefs Related to Integrated Science Education. Education Sciences, 11(6), 272. https://doi.org/10.3390/educsci11060272
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd Edition). Sage Publications Inc.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Henseler, J. (2018). Partial least squares path modeling: Quo vadis? Quality & Quantity, 52(1), 1–8. https://doi.org/10.1007/s11135-018-0689-6
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
Hmelo-Silver, C. E. (2004). Problem-Based Learning: What and How Do Students Learn? Educational Psychology Review, 16(3), 235–266. https://doi.org/10.1023/B:EDPR.0000034022.16470.f3
HMELO-SILVER, C. E., DUNCAN, R. G., & CHINN, C. A. (2007). Scaffolding and Achievement in Problem-Based and Inquiry Learning: A Response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42(2), 99–107. https://doi.org/10.1080/00461520701263368
Hofer, B. K. (2000). Dimensionality and Disciplinary Differences in Personal Epistemology. Contemporary Educational Psychology, 25(4), 378–405. https://doi.org/10.1006/ceps.1999.1026
Jansen, M., Schroeders, U., & Lüdtke, O. (2014). Academic self-concept in science: Multidimensionality, relations to achievement measures, and gender differences. Learning and Individual Differences, 30, 11–21. https://doi.org/10.1016/j.lindif.2013.12.003
Kang, M. J., Hsu, M., Krajbich, I. M., Loewenstein, G., McClure, S. M., Wang, J. T., & Camerer, C. F. (2009). The Wick in the Candle of Learning. Psychological Science, 20(8), 963–973. https://doi.org/10.1111/j.1467-9280.2009.02402.x
Kaushik, M. K., & Agrawal, D. (2021). Influence of technology readiness in adoption of e-learning. International Journal of Educational Management, 35(2), 483–495. https://doi.org/10.1108/IJEM-04-2020-0216
Kibga, E. S., Gakuba, E., & Sentongo, J. (2021). Developing Students’ Curiosity Through Chemistry Hands-on Activities: A Case of Selected Community Secondary Schools in Dar es Salaam, Tanzania. Eurasia Journal of Mathematics, Science and Technology Education, 17(5), em1962. https://doi.org/10.29333/ejmste/10856
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching. Educational Psychologist, 41(2), 75–86. https://doi.org/10.1207/s15326985ep4102_1
Kozma, R. (n.d.). Technology, innovation and educational change: A global perspective. Eugene.
Levin, T., & Wadmany, R. (2005). Changes in educational beliefs and classroom practices of teachers and students in rich technology-based classrooms[ 1 ]. Technology, Pedagogy and Education, 14(3), 281–307. https://doi.org/10.1080/14759390500200208
Lin, T.-J. (2021). Multi-dimensional explorations into the relationships between high school students’ science learning self-efficacy and engagement. International Journal of Science Education, 43(8), 1193–1207. https://doi.org/10.1080/09500693.2021.1904523
Litman, J. (2005). Curiosity and the pleasures of learning: Wanting and liking new information. Cognition and Emotion, 19(6), 793–814. https://doi.org/10.1080/02699930541000101
Liu, W., Li, X., & Li, G. (2023). The Contributions of Philosophy of Science in Science Education Research: a Literature Review. Science & Education, 34, 1203–1222. https://doi.org/10.1007/s11191-023-00485-w
Markula, A., & Aksela, M. (2022). The key characteristics of project-based learning: how teachers implement projects in K-12 science education. Disciplinary and Interdisciplinary Science Education Research, 4(1), 2. https://doi.org/10.1186/s43031-021-00042-x
er, R. E. (2004). Should There Be a Three-Strikes Rule Against Pure Discovery Learning? American Psychologist, 59(1), 14–19. https://doi.org/10.1037/0003-066X.59.1.14
er, R. E. (2009). Multimedia Learning. Cambridge University Press. https://doi.org/10.1017/CBO9780511811678
Nikolopoulou, K., Gialamas, V., Lavidas, K., & Komis, V. (2021). Teachers’ Readiness to Adopt Mobile Learning in Classrooms: A Study in Greece. Technology, Knowledge and Learning, 26(1), 53–77. https://doi.org/10.1007/s10758-020-09453-7
Noh, S. N. A., Azan, N., & Mohamed, H. (2020). Serious Games Requirements for Higher-Order Thinking Skills in Science Education. International Journal of Advanced Computer Science and Applications, 11(6). https://doi.org/10.14569/IJACSA.2020.0110627
Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). McGraw-Hill.
Papadakis, S., Zourmpakis, A.-I., & Kalogiannakis, M. (2023). Analyzing the Impact of a Gamification Approach on Primary Students’ Motivation and Learning in Science Education (pp. 701–711). https://doi.org/10.1007/978-3-031-26876-2_66
Parasuraman, A. (2000). Technology Readiness Index (Tri). Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001
Penuel, W. R., Bell, P., & Neill, T. (2020). Creating a system of professional learning that meets teachers’ needs. Phi Delta Kappan, 101(8), 37–41. https://doi.org/10.1177/0031721720923520
Plass, J. L., Homer, B. D., er, R. E., & Kinzer, C. K. (2020). Theoretical foundations of game-based and playful learning. The MIT Press.
Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., Kyza, E., Edelson, D., & Soloway, E. (2004). A Scaffolding Design Framework for Software to Support Science Inquiry. Journal of the Learning Sciences, 13(3), 337–386. https://doi.org/10.1207/s15327809jls1303_4
Rossi, I. V., de Lima, J. D., Sabatke, B., Nunes, M. A. F., Ramirez, G. E., & Ramirez, M. I. (2021). Active learning tools improve the learning outcomes, scientific attitude, and critical thinking in higher education: Experiences in an online course during the COVID ‐19 pandemic. Biochemistry and Molecular Biology Education, 49(6), 888–903. https://doi.org/10.1002/bmb.21574
Rutten, N., van Joolingen, W. R., & van der Veen, J. T. (2012). The learning effects of computer simulations in science education. Computers & Education, 58(1), 136–153. https://doi.org/10.1016/j.compedu.2011.07.017
Ruzaman, N. K., & Rosli, D. I. (2020). Inquiry-Based Education: Innovation in Participatory Inquiry Paradigm. International Journal of Emerging Technologies in Learning (IJET), 15(10), 4. https://doi.org/10.3991/ijet.v15i10.11460
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
Schommer, M. (2019). An emerging conceptualization of epistemological beliefs and their role in learning. In Beliefs about text and instruction with text. Routledge.
Schommer-Aikins, M. (2004). Explaining the Epistemological Belief System: Introducing the Embedded Systemic Model and Coordinated Research Approach. Educational Psychologist, 39(1), 19–29. https://doi.org/10.1207/s15326985ep3901_3
Schunk, D. H., & DiBenedetto, M. K. (2020). Motivation and social cognitive theory. Contemporary Educational Psychology, 60, 101832. https://doi.org/10.1016/j.cedpsych.2019.101832
Suprapto, N., Prahani, B. K., & Cheng, T. H. (2021). Indonesian Curriculum Reform in Policy and Local Wisdom: Perspectives from Science Education. Jurnal Pendidikan IPA Indonesia, 10(1), 69–80. https://doi.org/10.15294/jpii.v10i1.28438
Swarat, S., Ortony, A., & Revelle, W. (2012). Activity matters: Understanding student interest in school science. Journal of Research in Science Teaching, 49(4), 515–537. https://doi.org/10.1002/tea.21010
Sweller, J. (2010). Element Interactivity and Intrinsic, Extraneous, and Germane Cognitive Load. Educational Psychology Review, 22(2), 123–138. https://doi.org/10.1007/s10648-010-9128-5
Tashakkori, A., & Teddlie, C. (2010). SAGE Handbook of Mixed Methods in Social & Behavioral Research. SAGE Publications, Inc. https://doi.org/10.4135/9781506335193
Tsai, C.-C., Jessie Ho, H. N., Liang, J.-C., & Lin, H.-M. (2011). Scientific epistemic beliefs, conceptions of learning science and self-efficacy of learning science among high school students. Learning and Instruction. https://doi.org/10.1016/j.learninstruc.2011.05.002
von Stumm, S., Hell, B., & Chamorro-Premuzic, T. (2011). The Hungry Mind. Perspectives on Psychological Science, 6(6), 574–588. https://doi.org/10.1177/1745691611421204
Yang, D., Cai, Z., Wang, C., Zhang, C., Chen, P., & Huang, R. (2023). Not all engaged students are alike: patterns of engagement and burnout among elementary students using a person-centered approach. BMC Psychology, 11(1), 38. https://doi.org/10.1186/s40359-023-01071-z