Authors : Franklin B. Flores; Reynaldo H. Dalayap, Jr.; Allan Jay S. Cajandig

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/3z6xftr2

Scribd : https://tinyurl.com/2ad2srvp

DOI : https://doi.org/10.38124/ijisrt/25apr683

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Early intervention in education is vital to the progress and improvement of diverse learners that can significantly improve long-term educational outcomes and success in education like identifying and providing immediate intervention to students with learning difficulties, especially in mathematics subjects. Thus, this study aimed to develop Structural Equation Modeling, specifically the direct and indirect relationship among the students’ demographic profile, cognitive support, learning environments, student engagements, and performance in the first quarter in Pre-Calculus among grade 11 STEM students of Notre Dame of Tacurong College and Tacurong National High School, to provide a foundation for intervention in senior high students. The research instrument used in this study was a validated survey questionnaire created by the researcher and grouped into five latent variables, which were composed of thirty-three observed variables. The data was examined using frequency, percentage, mean, standard deviation, causal path analysis, and structural equation modeling. The results revealed that the students are diverse in their demographic profile. The predictors of students’ performance in pre-calculus (cognitive support, learning environment, and student engagement) need a very low to moderate intervention. Meanwhile, the level of the student performance was generally satisfactory. The demographic profile and student engagement have a direct connection with students' performance, whereas the cognitive support and learning environment have no association with students' performance. Thus, the link between cognitive support and learning environment has a direct impact on student engagement outcomes. Furthermore, the results revealed that student engagement mediates the relationship between cognitive support and student performance, demonstrating a substantial relationship. In contrast, there is no mediation effect between students' learning environment and their performance through student engagement. Furthermore, the structural equation modeling of five latent variables resulted in twenty-two indicators that indicate the reasonable level of convergent validity, discriminant validity, and significant relationship. Furthermore, the student engagement and student performance of the model indicate 35.6% and 9.3%, showing statistically significant support as a basis for intervention.

Keywords : Structural Equation Modeling; Basis for Intervention; Demographic Profile; Cognitive Support; Learning Environment; Student Engagement; and Students Performance in Pre-Calculus.

References :

1. Acharya, Nara. (2023). Effectiveness of Formative Evaluation on Mathematics    Achievement. Educational Journal. 2. 74-85. https://doi.org/10.3126/ej.v2i2.61697.
2. Acido, N. J. V., & Caballes, N. D. G. (2024). Assessing educational progress: A comparative analysis of PISA results (2018 vs. 2022) and HDI correlation in the Philippines. World Journal of Advanced Research and Reviews, 21(1),462474. https://doi.org/10.30574/wjarr.2024.21.1.0020
3. Bircan, M. A., & Akman, E. (2023). The relationship between students’ 21st-Century skills and academic performance in science and mathematics. Educational Policy Analysis and Strategic Research, 18(1), 273–291. https://doi.org/10.29329/epasr.2023.525.13
4. Bubou, G. M., & Job, G. C. (2020). Individual innovativeness, self-efficacy and e-learning readiness of students of Yenagoa study centre, National Open University of Nigeria. Journal of Research in Innovative Teaching & Learning, 15(1), 2–22. https://doi.org/10.1108/jrit-12-2019-0079
5. Capuno, R., Necesario, R., Etcuban, J. O., Espina, R., Padillo, G., & Manguilimotan, R. (2019). Attitudes, Study Habits, and Academic Performance of Junior High School Students in Mathematics. International Electronic Journal of Mathematics Education, 14(3), 547-561.https://doi.org/10.29333/iejme/5768
6. Hair, J. F., Howard, M. C., & Nitzl, C. (2019). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbus res.2019.11.069
7. Lamsal, H. (2024). Equitable pedagogical practices in learning mathematics at secondary schools in Nepal. The EFFORTS Journal of Education and Research, 5(1), 22–40. https://doi.org/10.3126/ejer.v5i1.65630
8. Mokhtar, K., Zaharudin, R., Amir, S., & Mokhtar, N. F. (2023). FAMILY INVOLVEMENT IN EDUCATION OF SPECIAL NEEDS STUDENTS: A SYSTEMATIC LITERATURE REVIEW. International Journal of Education Psychology and Counseling, 8(52), 244–257. https://doi.org/10.35631/ijepc.852020
9. Nacion, R. H. (2024). Development and validation of an assessment tool for measuring innovative thinking in STEM students. Applied Research and Innovation, 2(2), 1–12. https://doi.org/10.54536/ari.v2i2.3471
10. Nobis, M. J., & Caparroso, C. L. (2024). Bridging the gap: Examining parental involvement strategies and their impact on homework completion rates in mathematics. Alifmatika Jurnal Pendidikan Dan Pembelajaran Matematika, 6(1), 1–13. https://doi.org/10.35316/alifmatika.2024.v6i1.1-13
11. Prakash, K. P., & Selvakumari, K. (2021). Mathematical modelling and Big-Data analytics for student performance. Journal of Physics Conference Series, 1850(1), 012017. https://doi.org/10.1088/1742-6596/1850/1/012017
12. Purnomo, Y. W., Apriyanti, N., Mubarokah, S. A., Susilowati, & Anggraheni, W. A. (2022). The role of parental involvement and mathematics self-concept of elementary school students in online mathematics learning. The Education and Science Journal, 24(7), 110–125. https://doi.org/10.17853/1994-5639-2022-7-110-125
13. Rahayuningsih, S., Sirajuddin, S., & Nasrun, N. (2020). Cognitive flexibility: exploring students’ problem-solving in elementary school mathematics learning. JRAMathEdu (Journal of Research and Advances in Mathematics Education), 6(1), 59-70. https://doi.org/10.23917/jramathedu.v6i1.11630
14. Siaw, E. S., Shim, G. T. G., Azizan, F. L., & Shaipullah, N. M. (2021).         Understanding the Relationship between Students’ Mathematics Anxiety Levels and Mathematics Performances at the Foundation Level. Journal of Education and Learning, 10(1), 47- https://doi.org/10.5539/jel.v10n1p47
15. Soper, D. S. (2021). A-priori sample size calculator for structural equation models [Software]. 2021. URL: https://www. danielsoper. com/statcalc [accessed 2023-05-25].
16. Sun, S., Wu, X., & Xu, T. (2023). A theoretical framework for a mathematical cognitive model for adaptive learning systems. Behavioral Sciences, 13(5), 406. https://doi.org/10.3390/bs13050406
17. Tossavainen, T., Gröhn, J., Heikkinen, L., Kaasinen, A., & Viholainen, A. (2020). University mathematics students’ study habits and use of learning materials. LUMAT International Journal on Math Science and Technology Education, 8(1). https://doi.org/10.31129/lumat.8.1.1317
18. Udabah, Cordelia & Samuel Onyinyechi, Nneji. (2022). Study Habits: A Predictor of Secondary School Students' Performance in Mathematics.1. 184-197.
19. Villamor, Jimbo Juanito & Vistro-Yu, Catherine. (2023). Engaging High School      Students in Mathematical Modelling for Critical Citizenship.
20. Walberg, H. J. (1981). A psychological theory of educational productivity. In F.     H. Farley & N. Gordon (Eds.), Psychological and Education (pp. 81-110).        Chicago: National Society for the Study of Education.
21. Widiyawanti, (2024). The Importance of Setting the Classroom Learning                 Environment to Optimize its Function as a Learning Resource. Journal https://doi.org/10.52783/jes.2419
22. Winarso, W. (2016). Assessing the readiness of student learning activity and            learning outcome. Jouirnal Pencerahan, https://doi.org/10.13170/jp.10.2.5246