Assessing students' metacognitive strategies in e-learning and their role in academic performance

Moh Sulthon Amien, Universitas Muhammadiyah Surabaya, Indonesia
Achmad Hidayatullah, University of Szeged, Hungary

Abstract


The presence of e-learning as a new way of learning in the education system has attracted the interest of researchers worldwide. Nowadays, higher education still uses the e-learning system as a part of the learning method. The important issue in E-learning is how to promote academic performance. Metacognition theory argued that academic performance is determined by students’ metacognitive skills. Through metacognitive skills, students set their own goals, learning, monitoring, and evaluating in e-learning. Less is, however, known about how students involve metacognitive strategies in e-learning in the Indonesian context. Also, there is a scarcity of empirical evidence about the role of metacognitive strategies on academic performance in the Indonesian context. The purpose of this study is to assess students' metacognitive strategies and their impact on academic performance (i.e., engagement and achievement) in the e-learning context. One hundred and fifty students participated in the present study. Descriptive statistics and structural equation modeling were performed for data analysis. The result of this study revealed that students have high skills in metacognitive strategies in e-learning. Our study suggested that metacognitive strategies for self-regulated learning were found to be significantly associated with achievement and engagement in e-learning. In comparison, metacognitive for time and environment was only significantly associated with students' engagement but not with achievements. The contribution of this study to academic practice was explored.


Keywords


Achievements; E-learning; Engagements

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References


Ajisuksmo, C. R. P., & Saputri, G. R. (2017). The influence of attitudes towards mathematics, and metacognitive awareness on mathematics achievements. Creative Education, 08(03), 486–497. https://doi.org/10.4236/ce.2017.83037

Anthonysamy, L. (2021). The use of metacognitive strategies for undisrupted online learning: Preparing university students in the age of pandemic. Education and Information Technologies, 26(6), 6881–6899. https://doi.org/10.1007/s10639-021-10518-y

Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S. L. (2009). Measuring self-regulation in online and blended learning environments. Internet and Higher Education, 12(1), 1–6. https://doi.org/10.1016/j.iheduc.2008.10.005

Burin, D. I., Gonzalez, F. M., Barreyro, J. P., & Injoque-Ricle, I. (2020). Metacognitive regulation contributes to digital text comprehension in e-learning. Metacognition and Learning, 15(3), 391–410. https://doi.org/10.1007/s11409-020-09226-8

Chiu, T. K. F. (2022). Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(S1), S14–S30. https://doi.org/10.1080/15391523.2021.1891998

Ciascai, L., & Lavinia, H. (2011). Gender differences in metacognitive skills. A study of the 8th grade pupils in Romania. Procedia - Social and Behavioral Sciences, 29, 396–401. https://doi.org/10.1016/j.sbspro.2011.11.255

Coelho, V., Cadima, J., Pinto, A. I., & Guimarães, C. (2019). Self-regulation, engagement, and developmental functioning in preschool-aged children. Journal of Early Intervention, 41(2), 105–124. https://doi.org/10.1177/1053815118810238

Credé, M., & Kuncel, N. R. (2008). Study habits, skills, and attitudes: The third pillar supporting collegiate academic performance. Perspectives on Psychological Science, 3(6), 425–453. https://doi.org/10.1111/j.1745-6924.2008.00089.x

Csíkos, C. (2022). Metacognitive and non-metacognitive processes in arithmetic performance: Can there be more than one meta-level? Journal of Intelligence, 10(3). https://doi.org/10.3390/jintelligence10030053

Csíkos, C., & Steklács, J. (2010). Metacognition-based reading intervention programs among fourth-grade Hungarian students. In Efklides & A.Misailidi (Eds.), Trends and Prospects in Metacognition Research. Springer, Boston, MA. https://doi.org/https://doi.org/10.1007/978-1-4419-6546-2_16

Dignath, C., & Veenman, M. V. J. (2021). The role of direct strategy instruction and indirect activation of self-regulated learning — Evidence from classroom observation studies. Educational Psychology Review, 33(2), 489–533. https://doi.org/https://doi.org/10.1007/s10648-020-09534-0

Drigas, A., Mitsea, E., & Skianis, C. (2022). Metamemory: Metacognitive strategies for improved memory operations and the role of VR and mobiles. Behavioral Sciences, 12(11). https://doi.org/10.3390/bs12110450

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2019). Multivariate Data Analysis, Multivariate Data Analysis (Vol. 87, Issue 4). UK: Cengage Learning, EMEA.

Hidayatullah, A., Csíkos, C., & Wafubwa, R. N. (2023). The dimensionality of personal beliefs ; the investigation of beliefs based on the field study. Revista de Educación a Distancia (RED), 23(72), 1–26. https://doi.org/https://doi.org/10.6018/red.540251

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

Jain, S., & Dowson, M. (2009). Mathematics anxiety as a function of multidimensional self-regulation and self-efficacy. Contemporary Educational Psychology, 34(3), 240–249. https://doi.org/10.1016/j.cedpsych.2009.05.004

Kline, R. B. (2005). Principles and Practice of Structural Equation Modeling. The Guilford Press : New York and London.

Kong, Q. P., Wong, N. Y., & Lam, C. C. (2003). Student engagement in mathematics: Development of instrument and validation of construct. Mathematics Education Research Journal, 15(1), 4–21. https://doi.org/10.1007/BF03217366

Pintrich, P. R. (2015). Motivated Strategies for Learning Questionnaire (MSLQ). Mediterranean Journal of Social Sciences, 6(1), 156–164. https://doi.org/10.13140/RG.2.1.2547.6968

Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A Manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Arbor: University of Michigan, National Center for Research to Improve Postsecondary Teaching and Learning.

Shafiq, M. N. (2013). Gender gaps in mathematics, science and reading achievements in Muslim countries: a quantile regression approach. Education Economics, 21(4), 343–359. https://doi.org/10.1080/09645292.2011.568694

Sharif Nia, H., Azad Moghddam, H., Marôco, J., Rahmatpour, P., Allen, K. A., Kaur, H., Kaveh, O., Gorgulu, O., & Pahlevan Sharif, S. (2022). A psychometric lens for e-learning: examining the validity and reliability of the Persian Version of University Students’ Engagement Inventory (P-USEI). Asia-Pacific Education Researcher, 0123456789. https://doi.org/10.1007/s40299-022-00677-y

Standage, M., Duda, J. L., & Ntoumanis, N. (2005). A test of self-determination theory in school physical education. British Journal of Educational Psychology, 75(3), 411–433. https://doi.org/10.1348/000709904X22359

Sun, J. C. Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191–204. https://doi.org/10.1111/j.1467-8535.2010.01157.x

Valencia-Vallejo, N., López-Vargas, O., & Sanabria-Rodríguez, L. (2019). Effect of a metacognitive scaffolding on self-efficacy, metacognition, and achievement in e-learning environments. Knowledge Management and E-Learning, 11(1), 1–19. https://doi.org/10.34105/j.kmel.2019.11.001

Veenman, M. V. J., Van Hout-Wolters, B. H. A. M., & Afflerbach, P. (2006). Metacognition and learning: Conceptual and methodological considerations. Metacognition and Learning, 1(1), 3–14. https://doi.org/10.1007/s11409-006-6893-0

Vilkova, K. (2022). The promises and pitfalls of self-regulated learning interventions in MOOCs. Technology, Knowledge and Learning, 27(3), 689–705. https://doi.org/10.1007/s10758-021-09580-9

Wang, C. H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34(3), 302–323. https://doi.org/10.1080/01587919.2013.835779

Zhang, L. (2018). Gender differences in metacognitive and cognitive strategy use and reading test performance. In Metacognitive and Cognitive Strategy Use in Reading Comprehension (pp. 131–145). https://doi.org/10.1007/978-981-10-6325-1_6

Zhang, Y., Paquette, L., Bosch, N., Ocumpaugh, J., Biswas, G., Hutt, S., & Baker, R. S. (2022). The evolution of metacognitive strategy use in an open-ended learning environment: Do prior domain knowledge and motivation play a role? Contemporary Educational Psychology, 69(March), 102064. https://doi.org/10.1016/j.cedpsych.2022.102064




DOI: https://doi.org/10.21831/jitp.v10i2.60949

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