CORRELATION BETWEEN COGNITIVE ENGAGEMENT AND PERFORMANCE OF AFL UNDERGRADUATE STUDENTS IN VIRTUAL LEARNING ENVIRONMENT
Korelasi antara Penglibatan Kognitif dan Prestasi Pelajar Prasiswazah dalam Persekitaran Pembelajaran Maya
Keywords:Cognitive Engagement, Learning Performance, Virtual Learning Environment (VLE), Arabic, Structural Equation Modeling (SEM)
The virtual learning environment (VLEs) is becoming an essential instructional technology in this new era due to its effects and impacts on learning process. It has been implemented by many Malaysian higher educational institutions. This study aims to examine the correlation between cognitive engagement and performance among undergraduate students in an online learning environment. The using survey items adapted from Greene and Miller (1993) and data were collected from 216 Arabic Foreign Language (AFL) students. In evaluating the correlation and factors of cognitive engagement that affect student performance, this study employed the Partial Least Square Structural Equation Modelling (PLS-SEM) and a conceptual model was designed. The findings demonstrated positive correlation between cognitive engagement and student performance. The study indicated that the strongest predictor among the three factors of cognitive engagement is Self-Regulatory Strategy Use (SR), followed by Shallow Strategy Use (SSU) and Deep Strategy Use (DSU). As a result, cognitive engagement demonstrated to be critical predictor that can moderately affect students’ performance on the use of virtual learning environment. The coefficient of determination R2 values predicting performance are 0.588 (R2 = 0.588), which means can explain 58.8% of variance in students’ performance. This proportion is considered as moderate effect in affecting performance of AFL undergraduate students.
Abid, N., & Akhtar, M. (2020). Relationship between academic engagement and academic achievement: an empirical evidence of secondary school students. Journal of Educational Research, 23(1), 48.
Alanazi, A. A., Frey, B. B., Niileksela, C., Lee, S. W., Nong, A., & Alharbi, F. (2020). The role of task value and technology satisfaction in student performance in graduate-level online courses. TechTrends, 64(6), 922-930.
Anthonysamy, L., & Choo, A. (2021). Investigating Self-Regulated Learning Strategies for Digital Learning Relevancy. Malaysian Journal of Learning and Instruction, 18(1), 29-64.
Bayoumy, H. M. M., & Alsayed, S. (2021). Investigating Relationship of Perceived Learning Engagement, Motivation, and Academic Performance Among Nursing Students: A Multisite Study. Advances in Medical Education and Practice, 12, 351.
Bao, W. (2020). COVID‐19 and online teaching in higher education: A case study of Peking University. Human Behavior and Emerging Technologies, 2(2), 113-115.
Barlow, A. J. (2019). An Investigation of Student Cognitive Engagement in the STEM Classroom—A Compilation of Faculty and Student Perspectives.
Casimiro, L. T. (2016). Cognitive engagement in online intercultural interactions: Beyond analytics. International journal of information and education technology, 6(6), 441.
Cendaña, D. I., Ocay, A. B., Bustillo, N. V., & Cruz, J. D. (2019). The empirical study on the impact of student-centered learning application to cognition and social learning. In IOP Conference Series: Materials Science and Engineering (Vol. 482, No. 1, p. 012006). IOP Publishing.
Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural equation modeling: a multidisciplinary journal, 14(3), 464-504.
Chen, S. B. A. (2020). Examining the Effect of Self-Regulated Learning on Cognitive Engagement in Mastery-Based Online Courses: A Learning Analytics Perspective. The Ohio State University.
Exeter, D. J., Ameratunga, S., Ratima, M., Morton, S., Dickson, M., Hsu, D., & Jackson, R. (2010). Student engagement in very large classes: The teachers’ perspective. Studies in higher education, 35(7), 761-775.
DeVito, M. (2016). Factors Influencing Student Engagement (Unpublished master's thesis). Sacred Heart University, Fairfield, CT
Fredricks, J. A., Filsecker, M., & Lawson, M. A. (2016). Student engagement, context, and adjustment: Addressing definitional, measurement, and methodological issues.
Fredricks, J. A., & McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In Handbook of research on student engagement (pp. 763-782). Springer, Boston, MA.
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.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
García-Pérez, D., Fraile, J., & Panadero, E. (2021). Learning strategies and self-regulation in context: How higher education students approach different courses, assessments, and challenges. European Journal of Psychology of Education, 36(2), 533-550.
Greene, B. A. (2015). Measuring cognitive engagement with self-report scales: Reflections from over 20 years of research. Educational Psychologist, 50(1), 14-30.
Greene, B. A., Miller, R. B., Crowson, H. M., Duke, B. L., & Akey, K. L. (2004). Predicting high school students' cognitive engagement and achievement: Contributions of classroom perceptions and motivation. Contemporary educational psychology, 29(4), 462-482.
Greene, J. A., Copeland, D. Z., Deekens, V. M., & Seung, B. Y. (2018). Beyond knowledge: Examining digital literacy's role in the acquisition of understanding in science. Computers & Education, 117, 141-159.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long range planning, 46(1-2), 1-12.
Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long range planning, 45(5-6), 320-340.
Haron, H. N., Harun, H., Ali, R., Salim, K. R., & Hussain, N. H. (2015). Self-regulated learning strategies between the performing and non-performing students in statics. Proceedings of 2014 International Conference on Interactive Collaborative Learning, ICL 2014, (December), 802–805.
Hayati, H., Idrissi, M. K., & Bennani, S. (2020, July). Automatic classification for cognitive engagement in online discussion forums: text mining and machine learning approach. In International Conference on Artificial Intelligence in Education (pp. 114-118). Springer, Cham.
Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36-53.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems.
Hu, M., & Li, H. (2017). Student engagement in online learning: A review. In 2017 International Symposium on Educational Technology (ISET) (pp. 39-43). IEEE.
Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological methods, 3(4), 424.
Hudson KF. Nursing student engagement: student, classroom, and clinical engagement. Int J Nurs. 2015;4(1):44–52.
Humber, J. F. (2018). Student engagement in online courses: A grounded theory case study. The University of Alabama.
Hussain, S., Fangwei, Z., Siddiqi, A. F., Ali, Z., & Shabbir, M. S. (2018). Structural equation model for evaluating factors affecting quality of social infrastructure projects. Sustainability, 10(5), 1415.
Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & education, 104, 18-33.
Li, Y., & Lerner, R. M. (2013). Interrelations of behavioral, emotional, and cognitive school engagement in high school students. Journal of Youth and Adolescence, 42(1), 20-32.
Li, S., Zheng, J., & Lajoie, S. P. (2020). The relationship between cognitive engagement and students’ performance in a simulation-based training environment: an information-processing perspective. Interactive Learning Environments, 1-14.
Lilian, A. (2021). Self-Regulated Learning Strategies for Smart Learning: A Case of a Malaysian University. Asian Journal of Research in Education and Social Sciences, 3(1), 72-83.
Lochner, L., Wieser, H., Waldboth, S., & Mischo-Kelling, M. (2016). Combining traditional anatomy lectures with e-learning activities: how do students perceive their learning experience?. International journal of medical education, 7, 69.
McFarland, J., Cui, J., Holmes, J., & Wang, X. (2020). Trends in High School Dropout and Completion Rates in the United States: 2019. Compendium Report. NCES 2020-117. National Center for Education Statistics.
Mensink, P. J., & King, K. (2020). Student access of online feedback is modified by the availability of assessment marks, gender and academic performance. British Journal of Educational Technology, 51(1), 10-22.
Miller, P. A., Eisenberg, N., Fabes, R. A., & Shell, R. (1996). Relations of moral reasoning and vicarious emotion to young children's prosocial behavior toward peers and adults. Developmental psychology, 32(2), 210.
Nagadeepa, C. (2021). Students’ Understanding and Learning: Mediation Effects of Cognitive Engagement in Online Classes. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(6), 2932-2939.
Narad, A., & Abdullah, B. (2016). Academic performance of senior secondary school students: Influence of parental encouragement and school environment. Rupkatha Journal on Interdisciplinary Studies in Humanities, 8(2), 12-19.
Phillips, B. N., Turnbull, B. J., & He, F. X. (2015). Assessing readiness for self-directed learning within a non-traditional nursing cohort. Nurse education today, 35(3), e1-e7.
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of educational psychology, 82(1), 33.
Peng, C., & Daud, S. (2015). Exploring elementary special education (hearing impairment) teachers’ technological pedagogical content knowledge (TPACK). In 1st International Conference on Special Education, Bangkok, Thailand.
Rasouli, A., Rahbania, Z., & Attaran, M. (2016). Students' Readiness for E-Learning Application in Higher Education. Malaysian Online Journal of Educational Technology, 4(3), 51-64.
Rono, R. (2013). Factors Affecting Pupils' Performance in Public Primary Schools at Kenya Certificate of Primary Education Examination (Kcpe) in Emgwen Division, Nandi District, KENYA (Doctoral dissertation, University of Nairobi).
Reid-Martinez, K., & Grooms, L. D. (2018). Online learning propelled by constructivism. In Encyclopedia of Information Science and Technology, Fourth Edition (pp. 2588-2598). IGI Global.
Reeve, J. (2012). A self-determination theory perspective on student engagement. In Handbook of research on student engagement (pp. 149-172). Springer, Boston, MA.
Richardson, J. C., & Newby, T. (2006). The role of students' cognitive engagement in online learning. American Journal of Distance Education, 20(1), 23-37.
Rotgans, J. I., & Schmidt, H. G. (2011). Cognitive engagement in the problem-based learning classroom. Advances in health sciences education, 16(4), 465-479.
Sedaghat, M., Abedin, A., Hejazi, E., & Hassanabadi, H. (2011). Motivation, cognitive engagement, and academic achievement. Procedia-Social and Behavioral Sciences, 15, 2406-2410.
Shalaby, D., & Kamal, N. M. (2021). The Use of an Online Deep Learning Approach for Improving Self-Regulation. مجلة القراءة والمعرفة, 21(238), 1-41.
Li, S., & Lajoie, S. P. (2021). Cognitive engagement in self-regulated learning: an integrative model. European Journal of Psychology of Education, 1-20.
Simonova, I., & Poulova, P. (2013). Respondentsfeedback on online learning reflecting individual learning preferences. In proceedings of the international scientific conference. [LLU].
Susanti, Yunik. (2020). The Students' Engagement in EFL Online Class. Lingual: Journal of Language and Culture, [S.l.], v. 10, n. 2, p. 8,. ISSN 2716-3091
Trevors, G., Duffy, M., & Azevedo, R. (2014). Note-taking within MetaTutor: Interactions between an intelligent tutoring system and prior knowledge on note-taking and learning. Educational Technology Research and Development, 62(5), 507-528.
Topal, A. D. (2016). Examination of University Students' Level of Satisfaction and Readiness for E-Courses and the Relationship between Them. European Journal of Contemporary Education, 15(1), 7-23.
Valle, A., Regueiro, B., Núñez, J. C., Rodríguez, S., Piñeiro, I., & Rosário, P. (2016). Academic goals, student homework engagement, and academic achievement in elementary school. Frontiers in Psychology, 7, 463.
Wara, E., Aloka, P. J., & Odongo, B. C. (2018). Relationship between cognitive engagement and academic achievement among Kenyan Secondary School Students. Mediterranean Journal of Social Sciences, 9(2), 61-61.
Xie, K., Heddy, B. C., & Greene, B. A. (2019). Affordances of using mobile technology to support experience-sampling method in examining college students’ engagement. Computers & Education, 128, 183–198.
You, J. W., & Kang, M. (2014). The role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning. Computers & Education, 77, 125-133.
Yundayani, A., Abdullah, F., Tandiana, S. T., & Sutrisno, B. (2021). Students’ cognitive engagement during emergency remote teaching: Evidence from the Indonesian EFL milieu. Journal of Language and Linguistic Studies, 17(1).
Zhai, X., Gu, J., Liu, H., Liang, J. C., & Tsai, C. C. (2017). An Experiential Learning Perspective on Students' Satisfaction Model in a Flipped Classroom Context. J. Educ. Technol. Soc., 20(1), 198-210.
Zhu, X., Chen, A., Ennis, C., Sun, H., Hopple, C., Bonello, M., ... & Kim, S. (2009). Situational interest, cognitive engagement, and achievement in physical education. Contemporary educational psychology, 34(3), 221-229.
Zohud, N. W. I. (2015). Teaching Strategies and their Role on Students’ Engagement in Learning English (Doctoral dissertation).
How to Cite
Copyright (c) 2021 The Sultan Alauddin Sulaiman Shah Journal (JSASS)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.