The Silent Catalysts: How Engagement in Blended Learning Shapes Science Students’ Critical Thinking

Authors

  • Jovita Ridhani Department of Educational Technology, Faculty of Teacher Training and Education, Universitas Sebelas Maret, Surakarta, Indonesia
  • Fatma Sukmawati Department of Educational Technology, Faculty of Teacher Training and Education, Universitas Sebelas Maret, Surakarta, Indonesia https://orcid.org/0000-0002-8486-5137

DOI:

https://doi.org/10.21009/jtp.v27i3.60853

Keywords:

Blended Learning Engagement, Critical Thinking, Science Students, Online Learning, Distance Learning

Abstract

Blended learning becomes a central post-pandemic instructional model and understanding how learners engage within these environments is important for enhancing higher-order thinking skill. This study examines the extent to which students’ blended learning engagement predicts the critical thinking skills of high school science students, and whether socioeconomic status (SES) moderates this relationship. A quantitative correlational design was employed, involving 469 science students from Indonesian high schools that have consistently implemented blended learning. Data were collected through a 4-point Likert-scale questionnaire and analyzed using Spearman’s rank-order correlation and bootstrap regression. The results indicated that cognitive engagement was the strongest predictor of critical thinking (ρ = .793, p < .001; β = .751, p < .001), while emotional engagement had a positive but smaller effect (ρ = .291, p < .001; β = .066, p = .029), and behavioral engagement was insignificant (ρ ≈ .000, p = .998; β = –.028, p = .326). Simultaneously, the three dimensions of engagement explained 59.9% of the variability in critical thinking, with no significant moderating effect of SES. However, split-group correlations indicated that the correlation between blended learning engagement and critical thinking was stronger among students from low- (ρ = .512) and high- (ρ = .481) SES groups compared to those from the middle group (ρ = .386), indicating variation in effect magnitude but not direction, thereby clarifying the apparent contradiction with the non-significant moderation test. These findings confirmed cognitive engagement as the core component of reflective learning in blended learning, as well as highlighted the need for instructional designs that strengthen metacognitive regulation and epistemic autonomy for diverse learners.

References

Adams, D., Tan Hwee Joo, M., Sumintono, B., & Siew Pei, O. (2020). Blended learning engagement in higher education institutions: A differential item functioning analysis of students’ backgrounds. Malaysian Journal of Learning and Instruction, 17(1), 133–158.

Adedoyin, O. B., & Soykan, E. (2023). Covid-19 pandemic and online learning: the challenges and opportunities. In Interactive Learning Environments (Vol. 31, Issue 2, pp. 863–875). Routledge. https://doi.org/10.1080/10494820.2020.1813180

Akpinar, Y., & Aslan, Ü. (2015). Supporting Children’s Learning of Probability Through Video Game Programming. Journal of Educational Computing Research, 53(2), 228–259. https://doi.org/10.1177/0735633115598492

Almahasees, Z., Mohsen, K., & Amin, M. O. (2021). Faculty’s and Students’ Perceptions of Online Learning During COVID-19. Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.638470

Azubuike, O. B., Adegboye, O., & Quadri, H. (2021). Who gets to learn in a pandemic? Exploring the digital divide in remote learning during the COVID-19 pandemic in Nigeria. International Journal of Educational Research Open, 2, 100022. https://doi.org/10.1016/j.ijedro.2020.100022

Bach, K. M., Reinhold, F., & Hofer, S. I. (2025). Unlocking math potential in students from lower SES backgrounds – using instructional scaffolds to improve performance. Npj Science of Learning, 10(1), 66. https://doi.org/10.1038/s41539-025-00358-7

Bashir, A., Bashir, S., Rana, K., Lambert, P., & Vernallis, A. (2021). Post-Covid-19 adaptations; the Shifts towards online learning, hybrid course delivery and the implications for biosciences courses in the higher education setting. Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.711619

Bohnert, M., & Gracia, P. (2023). Digital use and socioeconomic inequalities in adolescent well‐being: Longitudinal evidence on socioemotional and educational outcomes. Journal of Adolescence, 95(6), 1179–1194. https://doi.org/10.1002/jad.12193

Castro, R. (2019). Blended learning in higher education: Trends and capabilities. Education and Information Technologies, 24(4), 2523–2546. https://doi.org/10.1007/s10639-019-09886-3

Çevik, M., & Senturk, C. (2019). Multidimensional 21th century skills scale: Validity and reliability study. Cypriot Journal of Educational Sciences, 14(1), 11–028. www.cjes.eu

Chang, D., Hwang, G.-J., Chang, S.-C., & Wang, S.-Y. (2021). Promoting students’ cross-disciplinary performance and higher order thinking: a peer assessment-facilitated STEM approach in a mathematics course. Educational Technology Research and Development, 69(6), 3281–3306. https://doi.org/10.1007/s11423-021-10062-z

Chin, W. M., Ahmad, N. A., Ismail, I. A., Alias, S. N., & Asri, A. S. (2025). A Serial Mediation Model on Trait Emotional Intelligence, Basic Psychological Needs, and Academic Motivation as Antecedents of Student Engagement. SAGE Open, 15(3). https://doi.org/10.1177/21582440251352752

Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences. Routledge. https://doi.org/10.4324/9780203771587

Creswell. (2014). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Pearson Australia.

D. Pagcamaan, L. R. (2024). Engagement and satisfaction in science employing blended earning in junior high school students. International Journal of Research Publications, 141(1). https://doi.org/10.47119/IJRP1001411120245996

de Bruin, A. B. H., Janssen, E. M., Waldeyer, J., & Stebner, F. (2025). Cognitive load and challenges in self-regulation: An introduction and reflection on the topical collection. Educational Psychology Review, 37(3), 65. https://doi.org/10.1007/s10648-025-10042-2

Dillenbourg, P. (2016). The Evolution of Research on Digital Education. International Journal of Artificial Intelligence in Education, 26(2), 544–560. https://doi.org/10.1007/s40593-016-0106-z

ElSayary, A. (2023). Students’ Active Engagement in Online Learning. In Overcoming Challenges in Online Learning (pp. 97–106). Routledge. https://doi.org/10.4324/9781003342335-12

Field, A. (2024). Discovering statistics using IBM SPSS statistics. Sage publications limited.

Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. . (2019). How to Design and Evaluate Research in Education (10th ed., Vol. 10). McGraw Hill Education.

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

García-Carmona, A. (2025). Scientific Thinking and Critical Thinking in Science Education . Science & Education, 34(1), 227–245. https://doi.org/10.1007/s11191-023-00460-5

Hair, J. F., Babin, B. J., Anderson, R. E., & Black, W. C. (2009). Multivariate data analysis. Pearson Prentice.

Halverson, L. R., & Graham, C. R. (2019). Learner Engagement in Blended Learning Environments: A Conceptual Framework. Online Learning, 23(2). https://doi.org/10.24059/olj.v23i2.1481

Hujjatusnaini, N., Corebima, A. D., Prawiro, S. R., & Gofur, A. (2022). The effect of blended project-based learning integrated with 21st-century skills on pre-service biology teachers’ higher-order thinking skills. Jurnal Pendidikan IPA Indonesia, 11(1), 104–118. https://doi.org/10.15294/jpii.v11i1.27148

Iglesias-Pradas, S., Hernández-García, Á., Chaparro-Peláez, J., & Prieto, J. L. (2021). Emergency remote teaching and students’ academic performance in higher education during the COVID-19 pandemic: A case study. Computers in Human Behavior, 119, 106713. https://doi.org/10.1016/j.chb.2021.106713

Kay, R., MacDonald, T., & DiGiuseppe, M. (2019). A comparison of lecture-based, active, and flipped classroom teaching approaches in higher education. Journal of Computing in Higher Education, 31(3), 449–471. https://doi.org/10.1007/s12528-018-9197-x

Li, W., Huang, J.-Y., Liu, C.-Y., Tseng, J. C. R., & Wang, S.-P. (2023). A study on the relationship between student’ learning engagements and higher-order thinking skills in programming learning. Thinking Skills and Creativity, 49, 101369. https://doi.org/10.1016/j.tsc.2023.101369

Papavlasopoulou, S., Giannakos, M. N., & Jaccheri, L. (2019). Exploring children’s learning experience in constructionism-based coding activities through design-based research. Computers in Human Behavior, 99, 415–427. https://doi.org/10.1016/j.chb.2019.01.008

Pasquinelli, E., Farina, M., Bedel, A., & Casati, R. (2021). Naturalizing Critical Thinking: Consequences for Education, Blueprint for Future Research in Cognitive Science. Mind, Brain, and Education, 15(2), 168–176. https://doi.org/10.1111/mbe.12286

Qaribilla, R., Indrajaya, K., & Mayawati, C. I. (2024). Digital Learning Inquality: The Role of Socioeconomic Status in Access to Online Education Resources. International Journal of Social and Human, 1(2), 51–58. https://doi.org/10.59613/55gdmt96

Qureshi, A., Wall, H., Humphries, J., & Bahrami Balani, A. (2016). Can personality traits modulate student engagement with learning and their attitude to employability? Learning and Individual Differences, 51, 349–358. https://doi.org/10.1016/j.lindif.2016.08.026

Rasheed, R. A., Kamsin, A., & Abdullah, N. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers and Education, 144. https://doi.org/10.1016/j.compedu.2019.103701

Salas‐Pilco, S. Z., Yang, Y., & Zhang, Z. (2022). Student engagement in online learning in Latin American higher education during the COVID‐19 pandemic: A systematic review. British Journal of Educational Technology, 53(3), 593–619. https://doi.org/10.1111/bjet.13190

Scherer, R., & Siddiq, F. (2019). The relation between students’ socioeconomic status and ICT literacy: Findings from a meta-analysis. Computers & Education, 138, 13–32. https://doi.org/10.1016/j.compedu.2019.04.011

Yang, J., Chen, Y., & Wang, Y. (2025). Exploring the Interplay of Motivation, Engagement and Critical Thinking Among EFL Learners: Evidence From Structural Equation Modelling. European Journal of Education, 60(3). https://doi.org/10.1111/ejed.70187

Zhou, X., & Tsai, C.-W. (2023). The Effects of Socially Shared Regulation of Learning on the Computational Thinking, Motivation, and Engagement in Collaborative Learning by Teaching. Education and Information Technologies, 28(7), 8135–8152. https://doi.org/10.1007/s10639-022-11527-1

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Published

2025-12-20

How to Cite

Ridhani, J., & Sukmawati, F. (2025). The Silent Catalysts: How Engagement in Blended Learning Shapes Science Students’ Critical Thinking. JTP - Jurnal Teknologi Pendidikan, 27(3), 909–923. https://doi.org/10.21009/jtp.v27i3.60853