Does Hybrid Mathematics Learning Evaluation Enhance Metacognitive Creative Problem-Solving? A Multi-Group Analysis by Gender
DOI:
https://doi.org/10.58524/oler.v6i1.1113Keywords:
Creative problem-solving, Evaluation, Gender, Hybrid learning, Mathematics learning, MetacognitionAbstract
Mathematics learning evaluation in hybrid learning environments is not only viewed as an assessment approach but also as a pedagogical strategy that facilitates students’ cognitive regulation and problem-solving processes. However, empirical evidence on the impact of hybrid mathematics learning evaluation on students’ metacognitive engagement in creative problem-solving, particularly regarding gender differences, remains limited. This study aims to explore the structural relationship between hybrid mathematics learning evaluation and metacognitive creative problem-solving, including gender differences. A partial least squares structural equation modeling (PLS-SEM) approach was used to collect data from 147 undergraduate mathematics students. Overall, the findings indicate a positive relationship between hybrid mathematics learning evaluation and metacognitive creative problem-solving across the full sample. In terms of gender differences, although the relationship is significant for both groups, it is stronger for male students. However, multi-group analysis revealed no significant difference in structural relationships between males and females, indicating model invariance. This study highlights the value of developing hybrid mathematics evaluation strategies to support students’ metacognitive abilities in creative problem-solving
References
Abdulrahman, A. O., & Rawf, K. M. H. (2022). Usability methodologies and data selection: Assessing the usability techniques on educational websites. International Journal of Electronics and Communications Systems, 2(2), 49–56. https://doi.org/10.24042/ijecs.v2i2.15045
Abedini, Y. (2022). Metacognition as a core skill for wise decision-making in higher education: Investigating gender differences. Journal of Applied Research in Higher Education, 14(2), 886–900. https://doi.org/10.1108/JARHE-11-2020-0400
Acosta-Gonzaga, E., & Ruiz-Ledesma, E. F. (2022). Students’ emotions and engagement in the emerging hybrid learning environment during the COVID-19 pandemic. Sustainability, 14(16), 10236. https://doi.org/10.3390/su141610236
Almasri, F. (2022). The impact of e-learning, gender groupings, and learning pedagogies in biology undergraduate female and male students’ attitudes and achievement. Education and Information Technologies, 27(6), 8329–8380. https://doi.org/10.1007/s10639-022-10967-z
Al-Rousan, A. H., Ayasrah, M. N., Khasawneh, M. A. S., & Khasawneh, Y. J. A. (2025). Development and validation of the metacognitive competency scale for college students in hybrid learning (MCS-HL): Insights from the network analysis perspective. Metacognition and Learning, 20(1), Article 14. https://doi.org/10.1007/s11409-025-09420-6
An, Y., Kaplan-Rakowski, R., Yang, J., Conan, J., Kinard, W., & Daughrity, L. (2024). Examining the impact of AI-based formative assessment on student engagement and metacognitive awareness. Computers & Education: Artificial Intelligence, 5, 100166. https://doi.org/10.1016/j.caeai.2024.100166
Angwaomaodoko, E. A. (2025). Alternative assessment methods in higher education: Evaluating their impacts on critical thinking and creativity. Traektoriâ Nauki, 11(8), 3022–3033. https://doi.org/10.22178/pos.121-60
Bandura, A., & Walters, R. H. (1977). Social learning theory. Prentice-Hall.
Barak, M., & Dori, Y. J. (2009). Enhancing higher order thinking skills among in-service science teachers via embedded assessment. Journal of Science Teacher Education, 20(5), 459–474. https://doi.org/10.1007/s10972-009-9141-z
Boekaerts, M. (1999). Self-regulated learning: Where we are today. International Journal of Educational Research, 31(6), 445–457. https://doi.org/10.1016/S0883-0355(99)00014-2
Cheng, V. M. (2011). Infusing creativity into Eastern classrooms: Evaluations from student perspectives. Thinking Skills and Creativity, 6(1), 67–87. https://doi.org/10.1016/j.tsc.2010.05.001
Chiu, M.-S. (2020). Gender differences in predicting STEM choice by affective states and behaviors in online mathematical problem solving: Positive-affect-to-success hypothesis. Journal of Educational Data Mining, 12(2), 48–77.
Efremova, N. F., Vasilyeva, I. V., & Kalimullin, A. M. (2019). Development of metacognitive skills and motivation in students through formative assessment. Eurasia Journal of Mathematics, Science and Technology Education, 15(6), em1707. https://doi.org/10.29333/ejmste/105285
Egara, F. O., & Mosimege, M. (2024). Effect of blended learning approach on secondary school learners’ mathematics achievement and retention. Education and Information Technologies, 29(15), 19863–19888. https://doi.org/10.1007/s10639-024-12651-w
Farida, F., Alamsyah, Y. A., Anggoro, B. S., Andari, T., & Lusiana, R. (2024). Rasch measurement validation of an assessment tool for measuring students’ creative problem-solving through the use of ICT. Pixel-Bit. Revista de Medios y Educación, 71, 83–106. https://doi.org/10.12795/pixelbit.107973
Frick, T. W., Chadha, R., Watson, C., & Zlatkovska, E. (2010). Improving course evaluations to improve instruction and complex learning in higher education. Educational Technology Research and Development, 58(2), 115–136. https://doi.org/10.1007/s11423-009-9131-z
Galvis, Á. H. (2018). Supporting decision-making processes on blended learning in higher education: Literature and good practices review. International Journal of Educational Technology in Higher Education, 15(1), 1–38. https://doi.org/10.1186/s41239-018-0106-1
Gamage, K. A., Gamage, A., & Dehideniya, S. C. (2022). Online and hybrid teaching and learning: Enhance effective student engagement and experience. Education Sciences, 12(10), 651. https://doi.org/10.3390/educsci12100651
Guo, W. (2024). Gender differences in teacher feedback and students’ self-regulated learning. Educational Studies, 50(3), 341–361. https://doi.org/10.1080/03055698.2021.1943648
Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective. Pearson.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications. https://doi.org/10.1007/978-3-030-80519-7
Hardy III, J. H., & Gibson, C. (2017). Gender differences in the measurement of creative problem-solving. The Journal of Creative Behavior, 51(2), 153–162. https://doi.org/10.1002/jocb.92
He, W., & Wong, W. (2021). Gender differences in the distribution of creativity scores: Domain-specific patterns in divergent thinking and creative problem solving. Frontiers in Psychology, 12, 626911. https://doi.org/10.3389/fpsyg.2021.626911
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
Hermita, N., Erlisnawati, Alim, J. A., Putra, Z. H., Mahartika, I., & Sulistiyo, U. (2024). Hybrid learning, blended learning or face-to-face learning: Which one is more effective in remediating misconception? Quality Assurance in Education, 32(1), 64–78. https://doi.org/10.1108/QAE-02-2023-0019
Idrizi, E., Filiposka, S., & Trajkovikj, V. (2023). Gender impact on STEM online learning: A correlational study of gender, personality traits and learning styles in relation to different online teaching modalities. Multimedia Tools and Applications, 82(19), 30201–30219. https://doi.org/10.1007/s11042-023-14908-x
Kay, R. H., & Knaack, L. (2009). Assessing learning, quality and engagement in learning objects: The Learning Object Evaluation Scale for Students (LOES-S). Educational Technology Research and Development, 57(2), 147–168. https://doi.org/10.1007/s11423-008-9094-5
Kwarikunda, D., Schiefele, U., Muwonge, C. M., & Ssenyonga, J. (2022). Profiles of learners based on their cognitive and metacognitive learning strategy use: Occurrence and relations with gender, intrinsic motivation, and perceived autonomy support. Humanities and Social Sciences Communications, 9(1), 1–12. https://doi.org/10.1057/s41599-022-01322-1
Lemieux, C. L., Collin, C. A., & Watier, N. N. (2019). Gender differences in metacognitive judgments and performance on a goal-directed wayfinding task. Journal of Cognitive Psychology, 31(4), 453–466. https://doi.org/10.1080/20445911.2019.1625905
Lin, W.-L., Hsu, K.-Y., Chen, H.-C., & Wang, J.-W. (2012). The relations of gender and personality traits on different creativities: A dual-process theory account. Psychology of Aesthetics, Creativity, and the Arts, 6(2), 112–123. https://doi.org/10.1037/a0026241
Malonisio, M. O. (2023). Blended learning modality in teaching statistics in a graduate program of a state university in the Philippines. Jurnal Ilmiah Peuradeun, 11(2), 403–424. https://doi.org/10.26811/peuradeun.v11i2.889
Martinez, M. E. (2006). What is metacognition? Phi Delta Kappan, 87(9), 696–699. https://doi.org/10.1177/003172170608700916
Mintu-Wimsatt, A. (2001). Traditional vs. technology-mediated learning: A comparison of students’ course evaluations. Marketing Education Review, 11(2), 63–73. https://doi.org/10.1080/10528008.2001.11488748
Nirmala, P., Suhardi, I., Kaswar, A. B., Surianto, D. F., B, M. F., Soeharto, S., & Lavicza, Z. (2025). Enhancing computational thinking skills through digital literacy and blended learning: The mediating role of learning motivation. Online Learning In Educational Research (OLER), 5(1), 9–24. https://doi.org/10.58524/oler.v5i1.504
Nurjannah, E., Mangesa, R. T., Fakhri, M. M., Fajar B, M., Sanatang, S., & Arifiyanti, F. (2025). enhancing digital learning outcomes: The combined impact of competence and psychological traits. Online Learning In Educational Research (OLER), 5(2), 277–288. https://doi.org/10.58524/oler.v5i2.513
Prasse, D., Webb, M., Deschênes, M., Parent, S., Aeschlimann, F., Goda, Y., Yamada, M., & Raynault, A. (2024). Challenges in promoting self-regulated learning in technology-supported learning environments: An umbrella review of systematic reviews and meta-analyses. Technology, Knowledge and Learning, 29(4), 1809–1830. https://doi.org/10.1007/s10758-024-09772-z
Pujayanto, P., Adi, D. W., & Hudha, M. N. (2025). Habits of mind: Student achievement in mathematical physics courses. Jurnal Ilmiah Pendidikan Fisika Al-Biruni, 14(1), 205–217. https://doi.org/10.24042/jipfalbiruni.v14i1.24294
Reeves, T. C., & Laffey, J. M. (1999). Design, assessment, and evaluation of a problem-based learning environment in undergraduate engineering. Higher Education Research & Development, 18(2), 219–232. https://doi.org/10.1080/0729436990180205
Shen, Y., Spencer, D., Tagsold, J., & Kim, H. (2025). Integrating cognition, self-regulation, motivation, and metacognition: A framework of post-pandemic flipped classroom design. Educational Technology Research and Development. Advance online publication. https://doi.org/10.1007/s11423-025-10485-y
Singh, J., Steele, K., & Singh, L. (2021). Combining the best of online and face-to-face learning: Hybrid and blended learning approach for COVID-19, post-vaccine, and post-pandemic world. Journal of Educational Technology Systems, 50(2), 140–171. https://doi.org/10.1177/00472395211047865
Siregar, H. S., Nurhamzah, N., Munir, M., & Fikri, M. (2025). Enhancing Islamic education through technology integration: A study of teaching practices in Indonesia. Jurnal Ilmiah Peuradeun, 13(2), 959–986. https://doi.org/10.26811/peuradeun.v13i2.1875
Siyahtaş, A., & Ceviz, E. (2025). The relationship between career satisfaction and life satisfaction: An Investigation from the perspective of efficacy of football coaches in Türkiye. Journal of Coaching and Sports Science, 4(2), 1–12. https://doi.org/10.58524/jcss.v4i2.563
Smit, R., Bachmann, P., Dober, H., & Hess, K. (2024). Feedback levels and their interaction with the mathematical reasoning process. The Curriculum Journal, 35(2), 184–202. https://doi.org/10.1002/curj.221
Suherman, S., Komarudin, K., & Supriadi, N. (2021). Mathematical creative thinking ability in online learning during the COVID-19 pandemic: A systematic review. Online Learning in Educational Research, 1(2), 75–80. https://doi.org/10.58524/oler.v1i2.49
Suherman, S., & Vidákovich, T. (2024). Role of creative self-efficacy and perceived creativity as predictors of mathematical creative thinking: Mediating role of computational thinking. Thinking Skills and Creativity, 53, 101591. https://doi.org/10.1016/j.tsc.2024.101591
Suherman, S., Vidákovich, T., Mujib, M., Hidayatulloh, H., Andari, T., & Susanti, V. D. (2025). The role of STEM teaching in education: An empirical study to enhance creativity and computational thinking. Journal of Intelligence, 13(7), 88. https://doi.org/10.3390/jintelligence13070088
Ukobizaba, F., Nizeyimana, G., & Mukuka, A. (2021). Assessment strategies for enhancing students’ mathematical problem-solving skills: A review of literature. Eurasia Journal of Mathematics, Science and Technology Education, 17(3), Article e9728. https://doi.org/10.29333/ejmste/9728
Urban, K., & Urban, M. (2023). How can we measure metacognition in creative problem-solving? Standardization of the MCPS scale. Thinking Skills and Creativity, 49, 101345. https://doi.org/10.1016/j.tsc.2023.101345
Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. https://doi.org/10.1006/ceps.1999.1015
Yen, J.-C., & Lee, C.-Y. (2011). Exploring problem solving patterns and their impact on learning achievement in a blended learning environment. Computers & Education, 56(1), 138–145. https://doi.org/10.1016/j.compedu.2010.08.012
Zeidner, M., & Stoeger, H. (2019). Self-regulated learning (SRL): A guide for the perplexed. High Ability Studies, 30(1–2), 9–51. https://doi.org/10.1080/13598139.2019.1589369
Zhang, L., Lei, Y., Pelton, T., Pelton, L. F., & Shang, J. (2024). An exploration of gendered differences in cognitive, motivational, and emotional aspects of game-based math learning. Journal of Computer Assisted Learning, 40(6), 2633–2649. https://doi.org/10.1111/jcal.12956
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Elsevier. https://doi.org/10.1016/B978-012109890-2/50031-7
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