Deconstructing learner engagement: An expanded construct model for higher education learners

Year 2023, , 395 - 412, 22.09.2023

Meral Şeker

https://doi.org/10.21449/ijate.1215747

Cited By: 1

Abstract

Despite the unanimous agreement regarding the positive outcomes of learner engagement, theorists and researchers draw attention to the disparate conceptualizations and structural models of “engagement” construct. The present study, in this respect, attempts to contribute to the development of a theoretical framework by suggesting a multidimensional overarching model for assessing higher education learner engagement. Following the descriptive research design, the study reports the initial model construction and validation results. The findings show significant differences from the earlier conceptualizations indicating a five-dimension model: academic-functional, cognitive, meta-cognitive, collaborative-social, and collaborative- academic engagement. While metacognitive engagement indicators form a distinct but integral dimension in the construct, the social dimension displays an idiosyncratic structure, implying that the multidimensional nature of the engagement construct has a situated nature. Pedagogical implications are discussed based on the engagement model validated.

Keywords

Higher education, Learner Engagement, Metacognitive Engagement, Engagement Indicators, Construct Model

Ethical Statement

Kocaeli University, Social and Human Sciences Ethics Committee, E-10017888-204.01.07-319718

References

• Appleton, J.J., Christenson, S.L., & Furlong, M.J. (2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45, 369–386.
• Appleton, J.J., Christenson, S.L., Kim, D., & Reschly, A.L. (2006). Measuring cognitive and psychological engagement: Validation of the student engagement instrument. Journal of School Psychology, 44, 427-445.
• Askham, P. (2008). Context and identity: Exploring adult learners’ experiences of higher education. Journal of Further and Higher Education, 32, 85–97.
• Australian Council for Educational Research, (2010). Doing more for learning: Enhancing engagement and outcomes. Australasian Student Engagement Report. ACER.
• Aubrey, S., King, J. & Almukhaild, H. (2020). Language learner engagement during speaking tasks: A longitudinal study. RELC Journal. https://doi.org/10.1177/0033688220945418
• Axelson, R.D., & Flick, A. (2011). Defining student engagement. Change, 43(1), 38–43.
• Carroll, M., Lindsey, S., Chaparro, M. & Winslow, B. (2021). An applied model of learner engagement and strategies for increasing learner engagement in the modern educational environment. Interactive Learning Environments, 29(5), 757-771.
• Chiu, T.K. (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(1), 14-30.
• Cleary, T.J., & Zimmerman, B.J. (2012). A cyclical self-regulatory account of student engagement: Theoretical foundations and applications. In S.L. Christenson, A. L Reschly & C. Wylie (Eds), Handbook of research on student engagement (pp. 237-257). Springer.
• Coates, H. (2010). Development of the Australasian survey of student engagement (AUSSE). Higher Education, 60, 1–17.
• Cobos, R., & Ruiz‐Garcia, J.C. (2021). Improving learner engagement in MOOCs using a learning intervention system: A research study in engineering education. Computer Applications in Engineering Education, 29(4), 733-749.
• Cohen, J. (1969). Statistical power analysis for the behavioral sciences. Academic Press.
• Dao, P., Nguyen, M.X.N.C., & Iwashita, N. (2021). Teachers’ perceptions of learner engagement in L2 classroom task-based interaction. The Language Learning Journal, 49(6), 711-724.
• Deng, R., Benckendorff, P., & Gannaway, D. (2020). Linking learner factors, teaching context, and engagement patterns with MOOC learning outcomes. Journal of Computer Assisted Learning, 36(5), 688-708.
• Finn, J.D. & Zimmer, K.S. (2012). Student engagement: What is it? Why does it matter? In S.L. Christenson, A.L. Reschly & C. Wylie (Eds), Handbook of research on student engagement (pp. 97-131). Springer.
• 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.
• Glanville, J.L., & Wildhagen, T. (2007). The measurement of school engagement: Assessing dimensionality and measurement invariance across race and ethnicity. Educational and Psychological Measurement, 67(6), 1019-1041.
• Hagel, P., Carr, R., & Devlin, M. (2012). Conceptualising and measuring student engagement through the Australasian Survey of Student Engagement (AUSSE): A critique. Assessment and Evaluation in Higher Education, 37(4), 475–486.
• Hiver, P., Zhou, S.A., Tahmouresi, S., Sang, Y., & Papi, M. (2020). Why stories matter: Exploring learner engagement and metacognition through narratives of the L2 learning experience. System, 91, 102260.
• Kahu, E.R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38(5), 758–773.
• Krause, K.L. (2012). Student engagement: A messy policy challenge in higher education. In I. Solomonides, A. Reid, & P. Petocz (Eds), Engaging with learning in higher education (pp. 457–474). Libri Publishers.
• Kuh, G.D. (2009). The national survey of student engagement: Conceptual and empirical foundations. New Directions for Institutional Research, 141, 5–21.
• Lam, S., Wong, B., Yang, H. & Liu, M. (2012). Understanding student engagement with a conceptual model. In S. Christenson, A. Reschly, and C. Wylie (Eds), Handbook of research on student engagement (pp.403–420). Springer.
• Macfarlane, I., Meach, P.M., & Leroy, B. S. (2014). Genetic counselling research: A practical guide. Oxford University Press.
• Martin, A.J. (2008). Enhancing student motivation and engagement: The effects of a multidimensional intervention. Contemporary Educational Psychology, 33(2), 239–269.
• Mazer, J.P. (2013). Validity of the student interest and engagement scales: Associations with student learning outcomes. Communication Studies, 64(2), 125-140.
• National Survey of Student Engagement. (2010). Major differences: Examining student engagement by field of study: Annual results 2010. Indiana University Center for Postsecondary Research.
• Nelson Laird, T.F., Chen, P.D. & Kuh, G.D. (2008). Classroom practices at institutions with higher than expected persistence rates: What student engagement data tell us”. New Directions for Teaching and Learning, 115, 85-99.
• Oxford, R.L. (2011). Teaching and researching language learning strategies. Pearson Longman.
• Padilla Rodriguez, B.C., Armellini, A., & Rodriguez Nieto, M.C. (2020). Learner engagement, retention and success: why size matters in massive open online courses (MOOCs). Open Learning: The Journal of Open, Distance and e-Learning, 35(1), 46-62.
• Pallant, J. (2001). SPSS survival manual: A step-by-step guide to data analysis using SPSS for windows. Open University Press.
• Pellas, N. (2014). The influence of computer self-efficacy, metacognitive self-regulation and self-esteem on student engagement in online learning programs: Evidence from the virtual world of Second Life. Computers in Human Behavior, 35, 157-170.
• Reschly, A., & Christenson, S.L. (2006). School completion. In G. Bear, & K. Minke (Eds), Children’s needs III: Development, prevention, and intervention. National Association of School Psychologists.
• Sato, M., & Storch, N. (2020). Context matters: Learner beliefs and interactional behaviors in an EFL vs. ESL context. Language Teaching Research. Advance online publication. https://doi.org/10.177/1362168820923582
• Schaufeli, W.B., Martinez, I.M., Pinto, A.M., Salanova, M., & Bakker, A.B. (2002). Burnout and engagement in university students: A cross-national study. Journal of Cross-Cultural Psychology, 33(5), 464-481.
• Şişman, M., & Turan, S. (2004). A study of correlation between job satisfaction and social-emotional loneliness of educational administrators in Turkish public schools. Eskisehir Osmangazi University Journal of Social Sciences, 5(1), 117-128.
• Stevens, J.P. (2002). Applied multivariate statistics for the social sciences. Lawrence Erlbaum Associates.
• Sun, Y., Guo, Y., & Zhao, Y. (2020). Understanding the determinants of learner engagement in MOOCs: An adaptive structuration perspective. Computers & Education, 157, 103963.
• Tabachnick, B.G., & Fidell, L.S. (2012). Using multivariate statistics. Allyn & Bacon.
• Taşdemir, H., & Yıldırım, T. (2017). Collaborative teaching from English language instructors’ perspectives. Journal of Language and Linguistic Studies, 13(2), 632-642.
• Tian, L. & Zhou, Y. (2020). Learner engagement with automated feedback, peer feedback and teacher feedback in an online EFL writing context. System, 91, 102247.
• Yang, L. (2020). Practice and exploration of online teaching during epidemic period. In 2020 6th International Conference on Social Science and Higher Education, 420-423.
• Zepke, N. (2014). Student engagement research in higher education: Questioning an academic orthodoxy. Teaching in Higher Education, 19(6), 697-708.
• Zhang, Z. (2022). Learner engagement and language learning: A narrative inquiry of a successful language learner”. The Language Learning Journal, 50(3), 378-392.
• Zhoc, K.C., Webster, B.J., King, R.B., Li, J.C., & Chung, T.S. (2019). Higher education student engagement scale (HESES): Development and psychometric evidence. Research in Higher Education, 60(2), 219-244.