academic self-efficacy, achievement, distance learning, subjective cognitive load


The COVID-19 global pandemic has forced the education process worldwide to change its form to distance learning. This empirical study contributes to recently limited knowledge about the remote learning process. The study aimed to determine how academic self-efficacy is related to subjective cognitive load to predict achievement results in different forms of distance learning. The research method used was a quasi-experimental pilot study. The research questions were: (1) What is the relationship between academic self-efficacy, subjective cognitive load, and achievement results in teacher-directed distance learning? (2) What is the relationship between academic self-efficacy, subjective cognitive load, and achievement in student-directed distance learning? (3) What is the difference between teacher-directed and student self-directed distance learning settings regarding relationships between academic self-efficacy, subjective cognitive load, and achievement results? The measurement of academic self-efficacy and subjective cognitive load in the context of task assessment results were compared in different distance learning settings in two independent groups of 9th graders. The results suggest a significant relationship between subjective cognitive load and achievement results in student self-directed but non-significant between all variables in teacher-directed distance learning settings. In contrast, settings themselves demonstrated no influence on any measured factors.


Download data is not yet available.


Al-Hunaiyyan, A., Bimba, A.T., Idris, N., Al-Sharhan, S. (2017). A cognitive knowledgebased framework for social and metacognitive support in mobile learning. Interdisciplinary Journal of Information, Knowledge, and Management, 12, 75-98. DOI:10.28945/3670

Alyushin, M.V., Kolobashkina, L.V. (2019). Monitoring of the current status of students as a means of increasing the effectiveness of educational process. The Education and science journal, 21(2), 176-197. DOI:10.17853/1994-5639-2019-2-176-197

Ayres, P. (2006). Using subjective measures to detect variations of intrinsic load within problems. Learning and Instruction,16, 389–400. DOI:

Baum, S., & McPherson, M. S. (2019). The Human Factor: The Promise & Limits of Online Education. Daedalus, 148, 235 -254. DOI:

Bandura, A. (1991). Social Cognitive Theory of Self-Regulation. Organizatioal Behaviour and Human Decision Processess, 50, 248-287. DOI:

Bandura, A. (2001). Social Cognitive Theory. Annual Agentive Perspective. Annual Review of Psychology, 52, 1-26. DOI:

Bouffard, T., & Bouchard, M. (2005). Influence of achievement goals and self‐efficacy on students' self‐regulation and performance. International Journal of Psychology, 40(6), 373-384. DOI:

Bouffard, T. & Couture, N. (2003). Motivational profile and academic achievement among students enrolled in different schooling tracks. Educational Studies, 29, 19-38. DOI: 10.1080/03055690303270

Buliņa, R. (2009). Perfekcionisma, pašefektivitātes un subjektīvās labklājības saistība. Rīga: Latvijas Universitāte. Maģistra darbs. Retrieved from

Chandler, P., & Sweller, J. (1992). The split-attention effect as a factor in the design of instruction. British Journal of Educational Psychology, 62, 233–246. DOI:

Chen, C.C., & Chen, C.Y. (2018). Exploring the effect of learning styles on learning achievement in a u-Museum. Interactive Learning Environments, 26(5): 664-681. DOI:

Cho, M. H., & Shen, D. (2013). Self-regulation in online learning. Distance Educ. 34, 290–301. DOI:

Crippen, K. J., Biesinger, K. D., Muis, K. R., & Orgill, M. (2009). The role of goal orientation and self-efficacy in learning from web-based worked examples. Journal of Interactive Learning Research, 20, 385–403. Retrieved from

Curum, B. & Khedo, K.K. (2020). Cognitive load management in mobile learning systems: principles and theories. Journal of Computers in Education, 1, 1-28. DOI: .

De Jong, T. (2010). Cognitive Load Theory, educational research, and instructional design: Some food for thought. Instructional Science, 38(2), 105–134. DOI 10.1007/s11251-009-9110-0

Education Law Amendment (2020). Latvijas Vēstnesis, 224, 19.11.2020. Retrieved from .

Firth, J., Torous, J., Stubbs, B., Firth, J. A., Steiner, G. Z., Smith, L., & Sarris, J. (2019). The "online brain": How the Internet may be changing our cognition. World Psychiatry, 18(2), 119-129. DOI: 10.1002/wps.20617

Gottschalk, F. (2019). Impacts of Technology Use on Children: Exploring Literature on the Brain, Cognition and Well-Being. OECD Education Working Papers, No. 195. DOI:

Griskevica, I. (2016). The Relationship among Verbal Ability and Demographic Factors in Latvia. In Laiviniece D. (Ed.), Language Acquisition: Problems and Perspectives, Part III: Research and Literacy. Cambridge Scholars Publishing, 122-134. Retrieved from

Griskevica, I. (2017). Psychological Traits in Teacher and Pupil Mutual Communication. 3rd International Conference on Lifelong Education and Leadership for All (ICLEL). Book of Conference Proceedings, 41-51. Retrieved from

Griskevica, I. (2018). Education and Decline of Cognitive Abilities in Late Adulthood . 4th International Conference on Lifelong Education and Leadership for All (ICLEL). Book of Conference Proceedings: 161-164. Retrieved from

Griskevica, I. (2020). The psychological impact of changing habits in contepmporary communication on education processes. Proceedings of the International Scientific Conference, Volume VII, May 22th -23th, 43-50. DOI: 10.17770/sie2020vol7.4813

Harackiewicz, J. M., Barron, K. E., Carter, S. M., Lehto, A. T., & Elliot, A. J. (1997). Predictors and consequences of achievement goals in the college classroom: Maintaining interest and making the grade. Journal of Personality and Social Psychology, 73, 1284-1295. DOI:

Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: a systematic review. Educational Research Review, 17, 63–84. DOI:

Hooft, G. J. (2018). New technologies and 21st-century children: Recent trends and outcomes. OECD Education Working Papers, No. 179, OECD Publishing. DOI: 10.1787/e071a505-en

Howe, N., & Strauss, W. (2000). Millennials rising. New York: Random House.

Huang, C. (2012). Gender differences in academic self-efficacy: a meta-analysis. European Journal of Psychology of Education, 28, 1–35. ERIC:

Huang, X. & Mayer, R. E. (2019). Adding Self-Efficacy Features to an Online Statistics Lesson. Journal of Educational Computing Research, 57(4), 1003-1037. DOI:

Kümmel, E., Moskaliuk, J., Cress, U., & Kimmerle, J. (2020). Digital Learning Environments in Higher Education: A Literature Review of the Role of Individual vs. Social Settings for Measuring Learning Outcomes. Education Sciences, 10(3), 78. DOI:

Joo, Y. J., Lim, K. Y., & Kim, J. (2013). Locus of control, self-efficacy, and task value as predictors of learning outcome in an online university context. Computer Education, 62, 149–158. DOI: 10.1016/j.compedu.2012.10.027

Li, C. & Lalani, F. (2020). The COVID-19 pandemic has changed education forever. World Economic Forum Agenda Publications. Retrieved from

Liou, P. Y., & Bulut, O. (2020). The effects of items format and cognitive domain on students’science performance in TIMSS 2011. Research in Science Education, 50, 99–121. DOI: https://l10.1007/s11165-017-9682-7

Lodge, J. M., & Harrison, W. (2019). The Role of Attention in Learning in the Digital Age. Yale Journal of Biology and Medicine, 92(1), 21–28. Retrieved from

Lodge J. M., & Horvath J. C. (2017). Science of learning and digital learning environments. In J.C. Horvath, J.M., Lodge, J.A.C., Hattie. From the laboratory to the classroom: Translating science of learning for teachers. Abingdon, UK: Routledge. DOI:

Lodge, J. M., Kennedy, G., & Lockyer, L. (2016). Special Issue: Brain, mind and educational technology. Australasian Journal of Educational Technology, 32(6), i-iii. DOI:

MacKenzie, M. L. (2019). Improving Learning Outcomes: Unlimited vs. Limited Attempts and Time for Supplemental Interactive Online Learning Activities. Journal of Curriculum and Teaching, 8(4), 36-45. ERIC: EJ1237507

Martin, F., Sun, T., & Westine, C.D. (2020). A systematic review of research on online teaching and learning from 2009 to 2018. Computer Education, Published online 2020 Sep 9. DOI: 10.1016/j.compedu.2020.104009

Mierlo, C. M., Jarodzka, H., Kirschner, Kirschner, F., & Kirschner, P. A. (2014). Cognitive Load Theory in E-Learning. Encyclopedia of cyber behavior, V(1) University at Albany, USA. DOI:

National Centre for Education Republic of Latvia (2020). The tasks of state examination. Retrieved from

Palghat, K., Horvath J. C., & Lodge, J. M. (2017). The hard problem of ‘educational neuroscience’. Trends in Neuroscience and Education, 6, 204–10. DOI: 10.1016/j.tine.2017.02.001

Paas, F., Tuovinen, J. I., Tabber, H., & Van Gerven, P. V. (2003). Cognitive Load Measurement as a Means to Advance Cognitive Load Theory. Educational Psychologist, 38(1), 63-71. Retrieved from

Paas, F., Ayres, P., & Pachman, M. (2008). Assessment of cognitive load in multimedia learning environments: Theory, methods, and applications. In D. H. Robinson & G. J. Schraw (Eds.), Recent innovations in educational technology that facilitate student learning, 11–35. Charlotte: Information Age. DOI: 10.4018/978-1-4666-0315-8.ch097

Paas, F., Renkl, A., & Sweller, J. (2010). Cognitive Load Theory and Instructional Design: Recent Developments Educational Psychologist, 38(1), 1-4. DOI:10.1023/B:TRUC.0000021806.17516.d0

Sahni, S. D., Polanin, J. R., Zhang, Q., Michaelson, L. E., Caverly, S., Polese, M. L., & Yang, J. (2021b). A What Works Clearinghouse rapid evidence review of distance learning programs. Technical appendix. Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance. ERIC: ED610886

Sternberg, R. J. (2020). Rethinking what we mean by intelligence. Sage Journals. Published online. DOI:

Stiller, K., D. & Bachmaier, R. (2018). Cognitive Loads in a Distance Training for Trainee Teachers. Frontiers in Education, 3. DOI:

Schwarzer, R., & Hallum, S. (2008). Perceived teacher self-efficacy as a predictor of job stress and burnout: Mediation analyses. Applied Psychology: An International Review. Special Issue: Health and Well-Being, 57, 152-171. DOI:10.1111/j.1464-0597.2008.00359.x

Schwarzer, R. & Jerusalem, M. (1995). Generalized Self-Efficacy scale. In J. Weinman, S. Wright, & M. Johnston, Measures in health psychology: A user’s portfolio. Causal and control beliefs (pp. 35-37). Windsor, UK: NFER-NELSON. Retrieved from

Schwarzer, R. (2014). Documentation of the General Self-Efficacy Scale. Retrieved from .

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. DOI:

Sweller, J. (1993). Some cognitive processes and their consequences for the organisation and presentation of information. Australian Journal of Psychology, 45(1), 1-8. DOI:

Sweller, J. & Merrienboer J. G., (2005). Cognitive Load Theory and Complex Learning:Recent Developments and Future Directions. Educational Psychology Review, 17(2), 136–150. DOI:10.1007/s10648-005-3951-0

Sweller, J., Van Merrienboer, J. J., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 32(2), 261-262. DOI: 10.1007/s10648-019-09465-5

Talsma, K., Schüza, B., Schwarzerc, R., & Norrisa, K. (2018). I believe, therefore I achieve (and vice versa): a meta-analytic cross-lagged panel analysis of self-efficacy and academic performance. Learning and Individual Differences, 61, 136–150. Retrieved form

Vasile, C., Marhan, A., M., Singer, F. M., & Stoicescu, D. (2011). Academic self-efficacy and cognitive load in students. Procedia-Social and Behavioral Sciences, 12, 478-482. DOI:

Wahabi, M. (2017). Study on the Impact of Motivation, Self-Efficacy and Learning Strategies of Faculty of Education Undergraduates Studying ICT Courses. The 4th International Postgraduate Research Colloquium IPRC Proceedings, 59-80. Retrieved from

Wilde, N., & Hsu, A. (2019). The influence of general self-efficacy on the interpretation of vicarious experience information within online learning. International Journal of Educational Technology in Higher Education, 16(26). DOI: 10.1186/s41239-019-0158-x

Yokyoma, S. (2019). Academic Self-Efficacy and Academic Performance in Online Learning: A Mini Review. Frontiers in Psychology, 9, 2794. DOI:

Yukselturk, E. & Bulut, S. (2007). Predictors for student success in an online course. Educational Technology and Society, 10, 71–83. ERIC: EJ814036

Yusuf, M. (2011). The impact of self-efficacy, achievement motivation, and selfregulated learning strategies on students’ academic achievement. Procedia Social and Behavioral Sciences, 15, 2623–2626. DOI: 10.1016/j.sbspro.2011.04.158

Zavizion, V. F., Bondarenko, I. M., Avierin, D. I., Hojouj, M. I., Davlietova, N.O., Cherednychenlo, N.O., Prokhach, A..V, Mashtaler, V. E., Dmytrenko, K. O., Lohvynenko, V. V., Kyslytsyna, V. S., Sukhoversha, O. A., Khvorostenko, Y. M., Elhajj, M. H., Suzdalev, P. L., Myroniuk, T. F., Kichtenko, I. N., Hrabovskyi, Y. V., Smolina, K. V., & Baranov, I. V. (2020). Distance learning: opportunities and challenges in quarantine. Medical Perspektives, 25(2), 4-12. DOI:

Zhampeissova, K., Gura, A., Vanina, E., & Egorova, Z. (2020). Academic Performance and Cognitive Load in Mobile Learning. Academic Performance and Cognitive Load in Mobile Learning, 14(21), 1-14. Retrieved from

Ziegler, E., Edelsbrunner, P. A., & Stern, E. (2020). The benefit of combining teacher-direction with contrasted presentation of algebra principles.European Journal of Psychology of Education. Advance online publication. DOI:




How to Cite

Griskevica, I., & Iltners, M. (2021). RELATIONSHIP BETWEEN ACADEMIC SELF-EFFICACY AND COGNITIVE LOAD FOR STUDENTS IN DISTANCE LEARNING. Education. Innovation. Diversity., 1(2), 31-40.