Faith Integrated Learning and Intelligence: Analyzing the Interaction Effect on Christian Students Mathematics Learning Outcomes
Abstract
Objective: This study aimed to examine the interaction effect between instructional strategies and levels of intelligence on students' mathematics learning oucomes at SMP Kristen GMIM Tomohon. The strategies compared included faith-integrated learning and conventional instruction, while students' intelligence levels were categorized as high or low. Method: A quantitative approach with a 2x2 factorial experimental design (treatment by level) was employed. A total of 42 students were assigned to either the experimental (faith-integrated) or control (conventional) group. Data were analyzed using two-way ANOVA with SPSS version 29. Results: The findings revealed a significant interaction between instructional strategies and intelligence levels on mathematics learning oucomes (significance = 0.009 < 0.05). This indicates that instructional effectiveness is influenced by students' intellectual capacities, highlighting the need to align teaching methods with learners' characteristics. Novelty: This study uniquely examines the interaction between faith-integrated instruction and students’ intelligence levels in mathematics learning. It also introduces “thinking in the heart”, a reflective faith-based approach that links mathematical understanding with spiritual values to foster holistic learning.
PDF Download: 25
SIMILARITY CHECK Download: 16
References
Agboola, A., & Tsai, K. C. (2012). Bring character education into classroom. European Journal of Educational Research, 1(2), 163–170. https://doi.org/10.12973/eu-jer.9.1.163 DOI: https://doi.org/10.12973/eu-jer.1.2.163
Ahmad, M., & Wilkins, S. (2025). Purposive sampling in qualitative research: A framework for the entire journey. Quality & Quantity, 59(2), 1461–1479. https://doi.org/10.1007/s11135-024-02022-5 DOI: https://doi.org/10.1007/s11135-024-02022-5
Bailey, R. (2006). Physical education and sport in schools: A review of benefits and outcomes. Journal of School Health, 76(8), 397–401. https://doi.org/10.1111/j.1744-6561.2006.00132.x DOI: https://doi.org/10.1111/j.1746-1561.2006.00132.x
Basilaia, G., & Kvavadze, D. (2020). Transition to online education in schools during a SARS-CoV-2 coronavirus (COVID-19) pandemic in Georgia. Pedagogical Research, 5(4). https://doi.org/10.29333/pr/7937 DOI: https://doi.org/10.29333/pr/7937
Bernard, R. M., Borokhovski, E., Schmid, R. F., Tamim, R. M., & Abrami, P. C. (2014). A meta-analysis of blended learning and technology use in higher education: From the general to the applied. Journal of Computing in Higher Education, 26(1), 87–122. https://doi.org/10.1007/s12528-013-9077-3 DOI: https://doi.org/10.1007/s12528-013-9077-3
Bower, M., Dalgarno, B., Kennedy, G. E., Lee, M. J. W., & Kenney, J. (2015). Design and implementation factors in blended synchronous learning environments: Outcomes from a cross-case analysis. Computers & Education, 86, 1–17. https://doi.org/10.1016/j.compedu.2015.03.006 DOI: https://doi.org/10.1016/j.compedu.2015.03.006
Bull, F. C., Al-Ansari, S. S., Biddle, S., Borodulin, K., Buman, M. P., Cardon, G., Carty, C., Chaput, J.-P., Chastin, S., & Chou, R. (2020). World Health Organization 2020 guidelines on physical activity and sedentary behaviour. British Journal of Sports Medicine, 54(24), 1451–1462. https://doi.org/10.1136/bjsports-2020-102955 DOI: https://doi.org/10.1136/bjsports-2020-102955
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. routledge. https://doi.org/10.4324/9780203771587 DOI: https://doi.org/10.4324/9780203771587
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications. https://doi.org/10.1177/1094428119864660
Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5–22. https://doi.org/10.1177/0047239520934018 DOI: https://doi.org/10.1177/0047239520934018
Dziuban, C., Graham, C. R., Moskal, P. D., Norberg, A., & Sicilia, N. (2018). Blended learning: the new normal and emerging technologies. International Journal of Educational Technology in Higher Education, 15(1), 3. https://doi.org/10.1186/s41239-017-0087-5 DOI: https://doi.org/10.1186/s41239-017-0087-5
Ekantini, A. (2022). The effectiveness of project based learning model for self regulated learning: A case study of post-pandemic elementary school science learning. Al-Bidayah: Jurnal Pendidikan Dasar Islam, 14(1), 95–114. https://doi.org/10.14421/albidayah.v14i1.889 DOI: https://doi.org/10.14421/albidayah.v14i1.889
Field, A. (2024). Discovering statistics using IBM SPSS statistics. Sage publications limited. https://doi.org/10.1177/2515245920979282 DOI: https://doi.org/10.1177/2515245920979282
Garrison, D. R. (2008). Blended learning in higher education: Framework, principles, and guidelines. Jossey-Bass. https://doi.org/10.1002/9781118269558 DOI: https://doi.org/10.1002/9781118269558
Graham, C. R., Woodfield, W., & Harrison, J. B. (2013). A framework for institutional adoption and implementation of blended learning in higher education. The Internet and Higher Education, 18, 4–14. https://doi.org/10.1016/j.iheduc.2012.09.003 DOI: https://doi.org/10.1016/j.iheduc.2012.09.003
Hake, R. R. (1999). American educational research association’s division d, measurement and research methodology: analyzing change/gain scores. USA: Woodland Hills. https://web.physics.indiana.edu/sdi/AnalyzingChange-Gain.pdf
Harrison, T., Morris, I., & Ryan, J. (2022). Teaching character in the digital age: Developing moral and civic virtues in young people. Educational Review, 74(3), 531–548. https://doi.org/10.1080/00131911.2020.1778264.
Hrastinski, S. (2019). What do we mean by blended learning? TechTrends, 63(5), 564–569. https://doi.org/10.1007/s11528-019-00375-5 DOI: https://doi.org/10.1007/s11528-019-00375-5
Keller, J. M. (2009). Motivational design for learning and performance: The ARCS model approach. Springer Science & Business Media. https://doi.org/10.1007/978-1-4419-1250-3 DOI: https://doi.org/10.1007/978-1-4419-1250-3
Kintu, M. J., Zhu, C., & Kagambe, E. (2017). Blended learning effectiveness: the relationship between student characteristics, design features and outcomes. International Journal of Educational Technology in Higher Education, 14(1), 7. https://doi.org/10.1186/s41239-017-0043-4 DOI: https://doi.org/10.1186/s41239-017-0043-4
Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1–47. https://doi.org/10.1177/016146811311500307 DOI: https://doi.org/10.1177/016146811311500307
Mercier, K., Centeio, E., Garn, A., Erwin, H., Marttinen, R., & Foley, J. (2021). Physical education teachers’ experiences with remote instruction during the initial phase of the COVID-19 pandemic. Journal of Teaching in Physical Education, 40(2), 337–342. https://doi.org/10.1123/jtpe.2020-0272 DOI: https://doi.org/10.1123/jtpe.2020-0272
Ntoumanis, N., Ng, J. Y. Y., Prestwich, A., Quested, E., Hancox, J. E., Thøgersen-Ntoumani, C., Deci, E. L., Ryan, R. M., Lonsdale, C., & Williams, G. C. (2021). A meta-analysis of self-determination theory-informed intervention studies in the health domain: effects on motivation, health behavior, physical, and psychological health. Health Psychology Review, 15(2), 214–244. https://doi.org/10.1080/17437199.2020.1718529 DOI: https://doi.org/10.1080/17437199.2020.1718529
Paulhus, D. L., & Vazire, S. (2007). The self-report method. Handbook of Research Methods in Personality Psychology, 1(2007), 224–239. https://doi.org/10.1002/9781118133880.hop202011 DOI: https://doi.org/10.1002/9781118133880.hop202011
Raes, A., Detienne, L., Windey, I., & Depaepe, F. (2020). A systematic literature review on synchronous hybrid learning: Gaps identified. Learning Environments Research, 23(3), 269–290. https://doi.org/10.1007/s10984-019-09303-z DOI: https://doi.org/10.1007/s10984-019-09303-z
Rasheed, R. A., Kamsin, A., & Abdullah, N. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education, 144, 103701. https://doi.org/10.1016/j.compedu.2019.103701 DOI: https://doi.org/10.1016/j.compedu.2019.103701
Rasmitadila, R., Aliyyah, R. R., Rachmadtullah, R., Samsudin, A., Syaodih, E., Nurtanto, M., & Tambunan, A. R. S. (2020). The perceptions of primary school teachers of online learning during the COVID-19 pandemic period. Journal of Ethnic and Cultural Studies, 7(2), 90–109. . https://doi.org/10.29333/ejecs/388 DOI: https://doi.org/10.29333/ejecs/388
Ryan, R. M., & Deci, E. L. (2000a). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020 DOI: https://doi.org/10.1006/ceps.1999.1020
Ryan, R. M., & Deci, E. L. (2000b). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68. https://doi.org/10.1037/0003-066X.55.1.68 DOI: https://doi.org/10.1037//0003-066X.55.1.68
Shadish, W. R. (2002). Experimental and quasi-experimental designs for generalized causal inference. Wadsworth Cengage Learning. https://doi.org/10.1093/oso/9780195108996.001.0001
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th Еd.). Boston: Allyn & Bacon/Pearson Education. https://doi. org/10.1207. https://doi.org/10.1037/10805-000 DOI: https://doi.org/10.1037/10805-000
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2 DOI: https://doi.org/10.1007/s11165-016-9602-2
Varea, V., & González-Calvo, G. (2021). Touchless classes and absent bodies: teaching physical education in times of Covid-19. Sport, Education and Society, 26(8), 831–845. https://doi.org/10.1080/13573322.2020.1791814 DOI: https://doi.org/10.1080/13573322.2020.1791814
Xiang, M., Zhang, Z., & Kuwahara, K. (2020). Impact of COVID-19 pandemic on children and adolescents’ lifestyle behavior larger than expected. Progress in Cardiovascular Diseases, 63(4), 531. https://doi.org/10.1016/j.pcad.2020.04.013 DOI: https://doi.org/10.1016/j.pcad.2020.04.013
Copyright (c) 2025 Aljuanika Ering (Author)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
PDF downloaded = 25 times
SIMILARITY CHECK downloaded = 16 times
























