Enhancing deep learning in blended learning environments by investigating the interplay of self-regulated learning, social presence, cognitive load, and learning outcomes among pre-service teachers

Meriyati Meriyati; Romlah Romlah; Eti Hadiati; Agus Jatmiko; Erwanto Erwanto

doi:10.24059/olj.v30i3.4991

Authors

Meriyati Meriyati UIN Raden Intan Lampung
Romlah Romlah UIN Raden Intan Lampung
Eti Hadiati UIN Raden Intan Lampung
Agus Jatmiko UIN Raden Intan Lampung
Erwanto Erwanto UIN Raden Intan Lampung

DOI:

https://doi.org/10.24059/olj.v30i3.4991

Keywords:

deep learning, self-regulated learning, social presence, higher education, blended learning, cognitive load

Abstract

Blended learning has become central to higher education, yet its capacity to promote deep learning depends on how cognitive, self-regulatory, and social factors are designed across online and face-to-face components. This study examined the relationships among cognitive load, self-regulated learning (SRL), social presence, deep learning, and learning outcomes among 450 Indonesian pre-service teachers in a structured blended learning environment. Data from survey responses and course-performance indicators were analyzed using partial least squares structural equation modeling (PLS-SEM) to test direct and mediated relationships. The results showed that cognitive load positively predicted deep learning but did not directly predict learning outcomes, indicating that cognitive demands can support achievement when they stimulate meaningful processing rather than overload learners. SRL also positively predicted deep learning but had no direct effect on outcomes, suggesting that regulation strategies improve performance mainly when translated into higher-order engagement. Deep learning strongly predicted learning outcomes and mediated the effects of cognitive load and SRL on achievement. Social presence did not significantly predict deep learning and negatively predicted learning outcomes, implying that interaction may be insufficient or distracting when not pedagogically structured. These findings identify deep learning as the central mechanism linking cognitive, self-regulatory, and social factors to academic performance. The study recommends blended learning designs that optimize cognitive challenge, scaffold SRL, and structure social interaction around purposeful inquiry, feedback, and knowledge construction.

References

Akyol, Z. (2009). Examining teaching presence, social presence, cognitive presence, satisfaction, and learning in online and blended course contexts [Doctoral dissertation, Middle East Technical University]. ProQuest Dissertations & Theses. https://search.proquest.com/openview/a3c668230fe0aad703a9422583bc4693/1?pq-origsite=gscholar&cbl=2026366&diss=y

Akyol, Z., & Garrison, D. R. (2008). The development of a community of inquiry over time in an online course: Understanding the progression and integration of social, cognitive, and teaching presence. Online Learning, 12(3-4), 3–22. https://doi.org/10.24059/olj.v12i3-4.1680

Asyhari, A., & Islamia, I. (2023). The influence of massive open online courses (MOOCs) and face-to-face learning on motivation and self-regulated learning (SRL). Journal of Educators Online, 20(1), 43–52. https://doi.org/10.9743/JEO.2023.20.1.2

Brockbank, R. B., & Feldon, D. F. (2024). Cognitive reappraisal: The bridge between cognitive load and emotion. Education Sciences, 14(8), Article 870. https://doi.org/10.3390/educsci14080870

Clark, J. (2024). Teaching one-pagers: Evidence-informed summaries for busy educational professionals. Hachette UK. https://books.google.com/books?id=8mkFEQAAQBAJ

Costley, J. (2019). The relationship between social presence and cognitive load. Interactive Technology and Smart Education, 16(2), 172–182. https://doi.org/10.1108/ITSE-12-2018-0107

Demir, Ö., Cinar, M., & Keskin, S. (2023). Participation style and social anxiety as predictors of active participation in asynchronous discussion forums and academic achievement. Education and Information Technologies, 28(9), 11313–11334. https://doi.org/10.1007/s10639-022-11517-3

Doo, M. Y., & Bonk, C. J. (2020). The effects of self-efficacy, self-regulation, and social presence on learning engagement in a large university class using flipped learning. Journal of Computer Assisted Learning, 36(6), 9971010. https://doi.org/10.1111/jcal.12455

Dunmoye, I. D., Rukangu, A., May, D., & Das, R. P. (2024). An exploratory study of social presence and cognitive engagement association in a collaborative virtual reality learning environment. Computers & Education: X Reality, 4, Article 100054. https://doi.org/10.1016/j.cexr.2024.100054

ElSayad, G. Drivers of undergraduate students’ learning perceptions in the blended learning environment: The mediation role of metacognitive self-regulation. Educ Inf Technol 29, 15737–15760 (2024). https://doi.org/10.1007/s10639-024-12466-9

Frey, R. F., Brame, C. J., Fink, A., & Lemons, P. P. (2022). Teaching discipline-based problem solving. CBE-Life Sciences Education, 21(2), Article fe1. https://doi.org/10.1187/cbe.22-02-0030

Garrison, D. R., Anderson, T., & Archer, W. (1999). 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

Hartelt, T., & Martens, H. (2024). Self-regulatory and metacognitive instruction regarding student conceptions: Influence on students’ self-efficacy and cognitive load. Frontiers in Psychology, 15, Article 1450947. https://doi.org/10.3389/fpsyg.2024.1450947

Haryanto, A. (2024). Blended learning models: Bridging traditional and digital educational practices. Innovative Journal of Educational Research and Insights, 1(2), 76–86.

Heilporn, G., Lakhal, S., & Bélisle, M. (2021). An examination of teachers’ strategies to foster student engagement in blended learning in higher education. International Journal of Educational Technology in Higher Education, 18, Article 25. https://doi.org/10.1186/s41239-021-00260-3

Jansen, A. (2021). Self-regulated learning strategies and student outcomes in ninth grade biology using blended learning strategies: A quantitative regression study [Doctoral dissertation, Northcentral University]. ProQuest Dissertations & Theses. https://search.proquest.com/openview/0659fdd31ab5af1d345b4982f8682955/1?pq-origsite=gscholar&cbl=18750&diss=y

Jeong, S., Rague, J., Litson, K., Feldon, D. F., Lawler, M. J., & Plummer, K. (2025). Effects of decision-based learning on student performance in introductory physics: The mediating roles of cognitive load and self-testing. Education and Information Technologies, 30, 4413-4433. https://doi.org/10.1007/s10639-024-12962-y

Jiang, Y., Wang, P., Li, Q., & Li, Y. (2022). Students’ intention toward self-regulated learning under blended learning setting: PLS-SEM approach. Sustainability, 14(16), Article 10140. https://doi.org/10.3390/su141610140

Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage.

Kirschner, P. (2002). Cognitive load theory: Implications of cognitive load theory on the design of learning. Learning and Instruction, 12(1), 1-10. https://doi.org/10.1016/S0959-4752(01)00014-7

Kreijns, K., Xu, K., & Weidlich, J. (2022). Social presence: Conceptualization and measurement. Educational Psychology Review, 34(1), 139-170. https://doi.org/10.1007/s10648-021-09623-8

Lee, S.-M. (2014). The relationships between higher order thinking skills, cognitive density, and social presence in online learning. The Internet and Higher Education, 21, 41-52. https://doi.org/10.1016/j.iheduc.2013.12.002

Leppink, J., Paas, F., Van der Vleuten, C. P. M., Van Gog, T., & Van Merriënboer, J. J. G. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058–1072. https://doi.org/10.3758/s13428-013-0334-1

Li, S., Chen, J., & Liu, S. (2024). The moderating effect of self-regulated learning skills on online learning behaviour in blended learning. Journal of Computer Assisted Learning, 40(6), 3125–3148. https://doi.org/10.1111/jcal.13059

Lobos, K., Cobo-Rendón, R., Bruna Jofré, D., & Santana, J. (2024). New challenges for higher education: Self-regulated learning in blended learning contexts. Frontiers in Education, 9, Article 1457367. https://doi.org/10.3389/feduc.2024.1457367

Longo, L. (2022). Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning. Brain Sciences, 12(10), Article 1416. https://doi.org/10.3390/brainsci12101416

Martin, S. (2018). Measuring cognitive load and cognition: Metrics for technology-enhanced learning. In Technology-enhanced and collaborative learning (pp. 77-106). Routledge. https://doi.org/10.4324/9781315270111-5

Miao, J., & Ma, L. (2022). Students' online interaction, self-regulation, and learning engagement in higher education: The importance of social presence to online learning. Frontiers in Psychology, 13, Article 815220. https://doi.org/10.3389/fpsyg.2022.815220

Misbah, Z., Gulikers, J., Widhiarso, W., & Mulder, M. (2022). Exploring connections between teacher interpersonal behaviour, student motivation, and competency level in competence-based learning environments. Learning Environments Research, 25(3), 641–661. https://doi.org/10.1007/s10984-021-09395-6

Müller, F. A., & Wulf, T. (2024). Differences in learning effectiveness across management learning environments: A cognitive load theory perspective. Journal of Management Education, 48(4), 802–828. https://doi.org/10.1177/10525629231200206

Munshi, A., Biswas, G., Baker, R., Ocumpaugh, J., Hutt, S., & Paquette, L. (2023). Analysing adaptive scaffolds that help students develop self-regulated learning behaviours. Journal of Computer Assisted Learning, 39(2), 351368. https://doi.org/10.1111/jcal.12761

Mystakidis, S., Berki, E., & Valtanen, J.-P. (2021). Deep and meaningful e-learning with social virtual reality environments in higher education: A systematic literature review. Applied Sciences, 11(5), Article 2412. https://doi.org/10.3390/app11052412

Onah, D. F. O., Pang, E. L. L., & Sinclair, J. E. (2022). Investigating self-regulation in the context of a blended learning computing course. The International Journal of Information and Learning Technology, 39(1), 50–69. https://doi.org/10.1108/IJILT-04-2021-0059

Panadero, E., Alonso-Tapia, J., García-Pérez, D., Fraile, J., Galán, J. M. S., & Pardo, R. (2021). Deep learning self-regulation strategies: Validation of a situational model and its questionnaire. Revista de Psicodidáctica (English Ed.), 26(1), 10–19. https://doi.org/10.1016/j.psicoe.2020.11.003

Parrish, C. W., Guffey, S. K., Williams, D. S., Estis, J. M., & Lewis, D. (2021). Fostering cognitive presence, social presence, and teaching presence with integrated online-team-based learning. TechTrends, 65(4), 473–484. https://doi.org/10.1007/s11528-021-00598-5

Ratan, R., Ucha, C., Lei, Y., Lim, C., Triwibowo, W., Yelon, S., Sheahan, A., Lamb, B., Deni, B., & Chen, V. H. H. (2022). How do social presence and active learning in synchronous and asynchronous online classes relate to students’ perceived course gains? Computers & Education, 191, Article 104621. https://doi.org/10.1016/j.compedu.2022.104621

Rovers, S. F. E., Clarebout, G., Savelberg, H. H. C. M., De Bruin, A. B. H., & Van Merriënboer, J. J. G. (2019). Granularity matters: Comparing different ways of measuring self-regulated learning. Metacognition and Learning, 14(1), 1–19. https://doi.org/10.1007/s11409-019-09188-6

Rui, L., Mohamad Nasri, N., & Mahmud, S. N. D. (2024). The role of self-directed learning in promoting deep learning processes: A systematic literature review. F1000Research, 13, Article 761. https://doi.org/10.12688/f1000research.150612.1

Russell, J. M., Baik, C., Ryan, A. T., & Molloy, E. (2022). Fostering self-regulated learning in higher education: Making self-regulation visible. Active Learning in Higher Education, 23(2), 97–113. https://doi.org/10.1177/1469787420982378

Seufert, T., Hamm, V., Vogt, A., & Riemer, V. (2024). The interplay of cognitive load, learners’ resources, and self-regulation. Educational Psychology Review, 36(2), Article 50. https://doi.org/10.1007/s10648-024-09890-1

Shao, J., Chen, Y., Wei, X., Li, X., & Li, Y. (2023). Effects of regulated learning scaffolding on regulation strategies and academic performance: A meta-analysis. Frontiers in Psychology, 14, Article 1110086. https://doi.org/10.3389/fpsyg.2023.1110086

Shea, P., Richardson, J., & Swan, K. (2022). Building bridges to advance the Community of Inquiry framework for online learning. Educational Psychologist, 57(3), 148–161. https://doi.org/10.1080/00461520.2022.2089989

Shi, H., & Lan, P. (2024). Exploring the factors influencing high school students’ deep learning of English in blended learning environments. Frontiers in Education, 9, Article 1339623. https://doi.org/10.3389/feduc.2024.1339623

Shi, Y., Tong, M., & Long, T. (2021). Investigating relationships among blended synchronous learning environments, students’ motivation, and cognitive engagement: A mixed methods study. Computers & Education, 168, Article 104193. https://doi.org/10.1016/j.compedu.2021.104193

Short, J., Williams, E., & Christie, B. (1976). The social psychology of telecommunications. John Wiley & Sons.

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

Skulmowski, A., & Xu, K. (2022). Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review, 34(1), 171–196. https://doi.org/10.1007/s10648-021-09624-7

Slack, F., Beer, M., Armitt, G., & Green, S. (2003). Assessment and learning outcomes: The evaluation of deep learning in an online course. Journal of Information Technology Education: Research, 2, 305–317. https://doi.org/10.28945/330

Supriyadi, A., Desy, D., Suharyat, Y., Santosa, T. A., & Sofianora, A. (2023). The effectiveness of STEM-integrated blended learning on Indonesia student scientific literacy: A meta-analysis. International Journal of Education and Literature, 2(1), 41–48. https://doi.org/10.55606/ijel.v2i1.53

Sweller, J. (2011). Cognitive load theory. Springer. https://doi.org/10.1007/978-1-4419-8126-4

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Measuring cognitive load. In Cognitive load theory (pp. 71-85). Springer. https://doi.org/10.1007/978-1-4419-8126-4_6

Turk, M., Heddy, B. C., & Danielson, R. W. (2022). Teaching and social presences supporting basic needs satisfaction in online learning environments: How can presences and basic needs happily meet online? Computers & Education, 180, Article 104432. https://doi.org/10.1016/j.compedu.2022.104432

Vargas-Mendoza, L., & Gallardo, K. (2023). Influence of self-regulated learning on the academic performance of engineering students in a blended-learning environment. International Journal of Engineering Pedagogy, 13(8), 84–99. https://doi.org/10.3991/ijep.v13i8.38481

Wang, F., Cheng, M., & Mayer, R. E. (2023). Improving learning-by-teaching without audience interaction as a generative learning activity by minimizing the social presence of the audience. Journal of Educational Psychology, 115(6), 783–797. https://doi.org/10.1037/edu0000801

Wang, T., & Lajoie, S. P. (2023). How does cognitive load interact with self-regulated learning? A dynamic and integrative model. Educational Psychology Review, 35(3), Article 69. https://doi.org/10.1007/s10648-023-09794-6

Wang, T., Li, S., Huang, X., Pan, Z., & Lajoie, S. P. (2023). Examining students' cognitive load in the context of self-regulated learning with an intelligent tutoring system. Education and Information Technologies, 28(5), 5697–5715. https://doi.org/10.1007/s10639-022-11357-1

Widjaja, G., & Aslan, A. (2022). Blended learning method in the view of learning and teaching strategy in geography study programs in higher education. Nazhruna: Jurnal Pendidikan Islam, 5(1), 22–36. https://doi.org/10.31538/nzh.v5i1.1852

Wu, X.-Y. (2025). Exploring the impact of blended collaborative learning on deep learning outcomes: A structural equation modeling approach. Education and Information Technologies. https://doi.org/10.1007/s10639-024-13202-z

Wut, T., & Xu, J. (2021). Person-to-person interactions in online classroom settings under the impact of COVID-19: A social presence theory perspective. Asia Pacific Education Review, 22(3), 371–383. https://doi.org/10.1007/s12564-021-09673-1

Xu, Z., Zhao, Y., Liew, J., Zhou, X., & Kogut, A. (2023). Synthesizing research evidence on self-regulated learning and academic achievement in online and blended learning environments: A scoping review. Educational Research Review, 39, Article 100510. https://doi.org/10.1016/j.edurev.2023.100510

Zhao, J., & Qin, Y. (2021). Perceived teacher autonomy support and students’ deep learning: The mediating role of self-efficacy and the moderating role of perceived peer support. Frontiers in Psychology, 12, Article 652796. https://doi.org/10.3389/fpsyg.2021.652796

Zhao, S., & Cao, C. (2023). Exploring relationship among self-regulated learning, self-efficacy, and engagement in blended collaborative context. SAGE Open, 13(1). https://doi.org/10.1177/21582440231157240

Zhong, Q., Wang, Y., Lv, W., Xu, J., & Zhang, Y. (2022). Self-regulation, teaching presence, and social presence: Predictors of students’ learning engagement and persistence in blended synchronous learning. Sustainability, 14(9), Article 5619. https://doi.org/10.3390/su14095619