Developing a Critical Inquiry-Based E-Educational Model (CIEM) to Enhance Critical Thinking Skills among High School Students: A Mixed-Methods Study in the Libyan Context

Authors

  • Layla Miloud Al-Souri Department of Mathematics, Faculty of Science and Natural Resources, Al-Jafara University, Libya Author
  • Asma Mustafa Abuadla Department of Mathematics, Faculty of Science and Natural Resources, Al-Jafara University, Libya Author
  • Jibreel Ramadan Mubarak Department of Computer Science, Faculty of Science and Natural Resources, Al-Jafara University, Libya Author
  • Siham Saleh Khalifa Al-Qablawi Department of Mathematics, Faculty of Science and Natural Resources, Al-Jafara University, Libya Author
  • Intisar Muammar Makari Department of Mathematics, Faculty of Science and Natural Resources, Al-Jafara University, Libya Author

DOI:

https://doi.org/10.65405/3n361e51

Keywords:

Critical Thinking; E-Educational Model; Critical Inquiry; Structural Equation Modeling; Mixed-Methods; Libya; Asynchronous Learning; Community of Inquiry

Abstract

Background: Despite institutional drives toward digitalization, a persistent gap separates the availability of technological resources from their pedagogically purposeful deployment in Libyan secondary schools. Didactic transmission methods continue to dominate instruction, compounded by intermittent infrastructural instability.

Objective: This study develops, validates, and empirically tests the Critical Inquiry-based E-Educational Model (CIEM) — a novel, low-bandwidth-optimized instructional framework designed to systematically enhance critical thinking skills among secondary school students.

Methodology: An explanatory sequential mixed-methods design (QUAN → qual) was employed with 240 secondary-school students across four Tripoli schools. Quantitative data were analyzed using ANCOVA and Structural Equation Modeling (SEM) via AMOS 26; qualitative data underwent reflexive thematic analysis guided by Lincoln and Guba's (1985) trustworthiness criteria.

Results: ANCOVA revealed a statistically significant group effect (F(1, 237) = 22.45, p < .001, Partial η² = .086, Cohen's d = 0.85). SEM confirmed a fully mediated pathway: CIEM significantly enhanced cognitive engagement (β = .54, p < .001), which in turn predicted critical thinking gains (β = .61, p < .001). Bootstrapped indirect effect: β = .33, 95% CI [.24, .42]. Power analysis (GPower 3.1; f = 0.25, α = .05, power = .95) confirmed the sample of n = 240 was adequate. Qualitative themes illuminated 'productive cognitive friction' as the primary mechanism of change.

Conclusion: CIEM offers a validated, context-sensitive pedagogical framework establishing that structured asynchronous inquiry — rather than technology per se — drives critical thinking development in resource-constrained settings.

Downloads

Download data is not yet available.

References

Abrami, P. C., Bernard, R. M., Borokhovski, E., Waddington, D. I., Wade, C. A., & Persson, T. (2015). Strategies for teaching students to think critically: A meta-analysis. Review of Educational Research, 85(2), 275–314. https://doi.org/10.3102/0034654314551063

Al-Smadi, O. S. (2022). The impact of blended learning on developing critical thinking skills among high school students in Jordan. International Journal of Instruction, 15(1), 45–62. https://doi.org/10.29333/iji.2022.1513a

Bjork, E. L., & Bjork, R. A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In M. A. Gernsbacher, R. W. Pew, L. M. Hough, & J. R. Pomerantz (Eds.), Psychology and the real world (pp. 56–64). Worth Publishers.

Azouz, A., Fawzi, M., Mohammed, I., Hamed, O., Maher, A., & Baddi, M. (2026). Influence of Electrolyte Chemistry and Electrode Material on Hydrogen Production Performance in Alkaline Water Electrolysis. Al-Farooq Journal of Sciences, 2(2), 49-66.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage.

Deci, E. L., & Ryan, R. M. (2000). The 'what' and 'why' of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). GPower 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059

Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2–3), 87–105. https://doi.org/10.1016/S1096-7516(00)00016-6

Guetterman, T. C., Fetters, M. D., & Creswell, J. W. (2015). Integrating quantitative and qualitative results in health science mixed methods research through joint displays. Annals of Family Medicine, 13(6), 554–561. https://doi.org/10.1370/afm.1865

Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Kapur, M. (2016). Examining productive failure, productive success, unproductive failure, and unproductive success in learning. Educational Psychologist, 51(2), 289–299. https://doi.org/10.1080/00461520.2016.1155457

Kim, Y., & Lee, J. (2022). Asynchronous online discussion and critical thinking in South Korean high schools. Computers & Education, 178, 104390. https://doi.org/10.1016/j.compedu.2021.104390.

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310

Ali, R. S. (2025). EFL Pre-Service Teachers’ Attitudes Towards Using AI Applications. Al-Farooq Journal of Sciences, 1(1), 93-109.

Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.

Liu, D., Kyndt, E., Baert, S., & Gijbels, D. (2023). Fostering critical thinking in education: A systematic review of the empirical evidence. Educational Research Review, 39, 100518. https://doi.org/10.1016/j.edurev.2023.100518

Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382–385. https://doi.org/10.1097/00006199-198611000-00017

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.

Nosek, B. A., Alter, G., Banks, G. C., & Yarkoni, T. (2015). Promoting an open research culture. Science, 348(6242), 1422–1425. https://doi.org/10.1126/science.aab2374

O'Donnell, C. L. (2008). Defining, conceptualizing, and measuring fidelity of implementation and its relationship to outcomes in K–12 curriculum intervention research. Review of Educational Research, 78(1), 33–84. https://doi.org/10.3102/0034654307313793

Piaget, J. (1952). The origins of intelligence in children. International Universities Press.

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.

Sweller, J. (2011). Cognitive load theory. In B. H. Ross (Ed.), Psychology of learning and motivation (Vol. 55, pp. 37–76). Academic Press. https://doi.org/10.1016/B978-0-12-387691-1.00002-8

Tiruneh, D. T., Verburgh, A., & Elen, J. (2016). Effectiveness of critical thinking instruction in higher education: A systematic review of intervention studies. Higher Education Studies, 6(3), 1–17. https://doi.org/10.5539/hes.v6n3p1

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

Watson, G., & Glaser, E. M. (1980). Watson-Glaser Critical Thinking Appraisal. Psychological Corporation.

Westland, J. C. (2010). Lower bounds on sample sizes in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476–487. https://doi.org/10.1016/j.elerap.2010.07.003

Zayed, A., & Hassan, M. (2021). E-learning readiness and critical thinking among Arab secondary students: Structural barriers and opportunities. International Journal of Educational Research, 108, 101793. https://doi.org/10.1016/j.ijer.2021.101793

Downloads

Published

2026-06-16

How to Cite

Developing a Critical Inquiry-Based E-Educational Model (CIEM) to Enhance Critical Thinking Skills among High School Students: A Mixed-Methods Study in the Libyan Context. (2026). Al-Farooq Journal of Sciences, 2(4), 525-540. https://doi.org/10.65405/3n361e51