The Role of Metacognitive Strategies in Blended LearningStudy Habits and Reading Comprehension

  1. Beatriz Ortega-Ruipérez 1
  1. 1 Universidad Internacional de La Rioja, España
Journal:
RIED: revista iberoamericana de educación a distancia

ISSN: 1138-2783

Year of publication: 2022

Volume: 25

Issue: 2

Pages: 219-238

Type: Article

More publications in: RIED: revista iberoamericana de educación a distancia

Abstract

Metacognitive strategies are essential, as they allow the learning process to be self-managed. This is especially important in higher education and blended learning because it requires greater independence. This study aims to determine the importance of metacognitive strategies as regards both study habits and reading comprehension in blended learning. For this purpose, metacognitive strategies are used through a digital tool in a blended learning context. SRSI-SR test was used to assess study habits and ARATEX-R was used to assess text reading before and after a master’s degree course. The study sample included 112 students from various disciplines; half of them used the tool as part of the research group, and the other half did not use it as part of the control group. The results show that the use of the metacognitive strategies has particularly facilitated the organization of the task regarding study habits. In reading comprehension, metacognitive strategies especially promoted motivation management, comprehension assessment, and planning. It is concluded that the use of metacognitive strategies has proven to be significantly effective, so these findings suggest the inclusion of metacognitive strategies in blended learning in order to improve study habits and reading comprehension in students and, thus, improve their learning outcomes. The conclusions obtained allow us to broaden our scientific knowledge about how these strategies influence learning.

Bibliographic References

  • Adams, A. M., Wilson, H., Money, J., Palmer-Conn, S., & Fearn, J. (2019). Student engagement with feedback and attainment: the role of academic self-efficacy. Assessment & Evaluation in Higher Education, 45(2), 317-329. https://doi.org/10.1080/02602938.2019.1640184
  • Akamatsu, D., Nakaya, M., & Koizumi, R. (2019). Effects of metacognitive strategies on the self-regulated learning process: The mediating effects of self-efficacy. Behavioral Sciences, 9(12), 128. https://doi.org/10.3390/bs9120128
  • Alzaid, M., & Hsiao, S. (2019). Utilising problem-solving: from self-assessment to self-regulating. New Review of Hypermedia and Multimedia, 25(3), 222-244. https://doi.org/10.1080/13614568.2019.1705922
  • Anthonysamy, L., Koo, A. C., & Hew, S. H. (2020). Self-Regulated learning strategies and non-academic outcomes in higher education blended learning environments: a one decade review. Education and Information Technologies, 25, 3677-3704. https://doi.org/10.1007/s10639-020-10134-2
  • Bahri, A., Idris, I. S., Muis, H., Arifuddin, M., & Fikri, M., J., N. (2021). Blended Learning Integrated with Innovative Learning Strategy to Improve Self-Regulated Learning. International. Journal of Instruction, 14(1), 779-794. https://doi.org/10.29333/iji.2021.14147a
  • Blau, I., & Shamir-Inbal, T. (2017). Re-designed flipped learning model in an academic course: The role of co-creation and co-regulation. Computers & Education, 115, 69-81. https://doi.org/10.1016/j.compedu.2017.07.014
  • Broadbent, J. (2017). Comparing online and blended learner's self-regulated learning strategies and academic performance. The Internet and Higher Education, 33, 24-32. https://doi.org/10.1016/j.iheduc.2017.01.004
  • Broadbent, J., & Fuller-Tyszkiewicz, M. (2018). Profiles in self-regulated learning and their correlates for online and blended learning students. Educational technology research and development, 66(6), 1435-1455. https://doi.org/10.1007/s11423-018-9595-9
  • Bull, S., & Kay, J. (2010). Open Learner Models. In R. Nkambou, J. Bourdeau y R. Mizoguchi (Eds.), Advances in Intelligent Tutoring Systems. Studies in Computational Intelligence, 308. Springer. https://doi.org/10.1007/978-3-642-14363-2_15
  • Carroll, D. (2020). Observations of student accuracy in criteria-based self-assessment. Assessment & Evaluation in Higher Education, 45(8), 1088-1105. https://doi.org/10.1080/02602938.2020.1727411
  • Chen, P. P., & Bonner, S. M. (2020). A framework for classroom assessment, learning, and self-regulation. Assessment in Education: Principles, Policy & Practice, 27(4), 373-393. https://doi.org/10.1080/0969594X.2019.1619515
  • Chou, C. Y., & Zou, N. B. (2020). An analysis of internal and external feedback in self-regulated learning activities mediated by self-regulated learning tools and open learner models. International Journal of Educational Technology in Higher Education, 17(1), 1-27. https://doi.org/10.1186/s41239-020-00233-y
  • Cleary, T. J. (2006). The development and validation of the Self-Regulation Strategy Inventory—Self-Report. Journal of School Psychology, 44(4), 307-322. https://doi.org/10.1016/j.jsp.2006.05.002
  • Colthorpe, K., Sharifirad, T., Ainscough, L., Anderson, S., & Zimbardi, K. (2018). Prompting undergraduate students’ metacognition of learning: implementing ‘meta-learning’assessment tasks in the biomedical sciences. Assessment & Evaluation in Higher Education, 43(2), 272-285. https://doi.org/10.1080/02602938.2017.1334872
  • Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
  • Dixon, H., Hawe, E., & Hamilton, R. (2020). The case for using exemplars to develop academic self-efficacy. Assessment & Evaluation in Higher Education, 45(3), 460-471. https://doi.org/10.1080/02602938.2019.1666084
  • Fokkens-Bruinsma, M., Vermue, C., Deinum, J. F., & van Rooij, E. (2020). First-year academic achievement: the role of academic self-efficacy, self-regulated learning and beyond classroom engagement. Assessment & Evaluation in Higher Education, 46(7), 1115-1126. https://doi.org/10.1080/02602938.2020.1845606
  • Fraile, J., Gil-Izquierdo, M., Zamorano-Sande, D., & Sánchez-Iglesias, I. (2020). Self-regulated learning and formative assessment process on group work. Relieve: Revista Electrónica de Investigación y Evaluación Educativa, 26(1), 7. https://doi.org/10.7203/relieve.26.1.17402
  • Hawe, E., & Dixon, H. (2017). Assessment for learning: a catalyst for student self-regulation. Assessment & Evaluation in Higher Education, 42(8), 1181-1192. https://doi.org/10.1080/02602938.2016.1236360
  • Hernández, A., & Camargo, Á. (2017). Adaptación y validación del Inventario de Estrategias de Autorregulación en estudiantes universitarios. Suma Psicológica, 24(1), 9-16. https://doi.org/10.1016/j.sumpsi.2017.02.001
  • Hooshyar, D., Kori, K., Pedaste, M., & Bardone, E. (2019). The potential of open learner models to promote active thinking by enhancing self‐regulated learning in online higher education learning environments. British Journal of Educational Technology, 50(5), 2365-2386. https://doi.org/10.1111/bjet.12826
  • Kay, J., Halin, Z., Ottomann, T., & Razak, Z. (1997). Learner know thyself: Student models to give learner control and responsibility. In Proceedings of International Conference on Computers in Education (pp. 17-24).
  • Kickert, R., Meeuwisse, M., M. Stegers-Jager, K., V. Koppenol-Gonzalez, G., R. Arends, L., & Prinzie, P. (2019). Assessment policies and academic performance within a single course: the role of motivation and self-regulation. Assessment & Evaluation in Higher Education, 44(8), 1177-1190. https://doi.org/10.1080/02602938.2019.1580674
  • Lluch, L., & Portillo, M. C. (2018). La competencia de aprender a aprender en el marco de la educación superior. Revista Iberoamericana de Educación, 78(2), 59-76. https://doi.org/10.35362/rie7823183
  • Muijs, D., & Bokhove, C. (2020). Metacognition and Self-Regulation: Evidence Review. London: Education Endowment Foundation. https://educationendowmentfoundation.org.uk/evidence-summaries/evidence-reviews/metacognition-and-self-regulation-review/
  • Ng, E. M. (2018). Integrating self-regulation principles with flipped classroom pedagogy for first year university students. Computers & Education, 126, 65-74. https://doi.org/10.1016/j.compedu.2018.07.002
  • Nieminen, J. H., & Tuohilampi, L. (2020). ‘Finally studying for myself’–examining student agency in summative and formative self-assessment models. Assessment & Evaluation in Higher Education, 45(7), 1031-1045. https://doi.org/10.1080/02602938.2020.1720595
  • Núñez, J. C., Amieiro, N., Álvarez, D., García, T., & Dobarro, A. (2015). Escala de Evaluación de la Autorregulación del Aprendizaje a partir de Textos (ARATEX-R). European Journal of Education and Psychology, 8(1), 9-22. https://doi.org/10.1016/j.ejeps.2015.10.002
  • Onah, D. F., Pang, E. L., & Sinclair, J. E. (2020). Cognitive optimism of distinctive initiatives to foster self-directed and self-regulated learning skills: A comparative analysis of conventional and blended-learning in undergraduate studies. Education and Information Technologies, 25(5), 4365-4380. https://doi.org/10.1007/s10639-020-10172-w
  • Ortega-Ruipérez, B., & Castellanos, A. (2021). Design and development of a digital tool for metacognitive strategies in self-regulated learning. In EDULEARN21 Proceedings. https://doi.org/10.21125/edulearn.2021
  • Panadero, E., Broadbent, J., Boud, D., & Lodge, J. M. (2018). Using formative assessment to influence self- and co-regulated learning: The role of evaluative judgement. European Journal of Psychology of Education, 34(3), 535-557. https://doi.org/10.1007/s10212-018-0407-8
  • Panadero, E., Jonsson, A., & Botella, J. (2017). Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses. Educational Research Review, 22, 74-98. https://doi.org/10.1016/j.edurev.2017.08.004
  • Pardo, A., Han, F., & Ellis, R. A. (2016). Combining university student self-regulated learning indicators and engagement with online learning events to predict academic performance. IEEE Transactions on Learning Technologies, 10(1), 82-92. https://doi.org/10.1109/TLT.2016.2639508
  • Pintrich, P. (2004). A conceptual framework for assessing motivation and SRL in college students. Educational Psychology Review, 16(4), 385-407. https://doi.org/10.1007/s10648-004-0006-x
  • Sáiz, M. C., García-Osorio, C. I., & Díez-Pastor, J. F. (2019). Differential efficacy of the resources used in B-learning environments, Psicothema, 31(2), 170-178. https://doi.org/10.7334/psicothema2018.330
  • Sáiz, M. C., Marticorena, R., García-Osorio, C. I., & Díez-Pastor, J. F. (2017). How Do B-Learning and Learning Patterns Influence Learning Outcomes? Frontiers in Psychology, 8, 745. https://doi.org/10.3389/fpsyg.2017.00745
  • Schumacher, C., & Ifenthaler, D. (2018). Features students really expect from learning analytics. Computers in human behavior, 78, 397-407. https://doi.org/10.1016/j.chb.2017.06.030
  • Schwam, D., Greenberg, D., & Li, H. (2020). Individual Differences in Self-regulated Learning of College Students Enrolled in Online College Courses. American Journal of Distance Education, 35(2), 133-151. https://doi.org/10.1080/08923647.2020.1829255
  • Tai, J., Ajjawi, R., Boud, D., Dawson, P., & Panadero, E. (2018). Developing evaluative judgement: enabling students to make decisions about the quality of work. Higher Education, 76(3), 467-481. https://doi.org/10.1007/s10734-017-0220-3
  • Vanslambrouck, S., Zhu, C., Pynoo, B., Lombaerts, K., Tondeur, J., & Scherer, R. (2019). A latent profile analysis of adult students’ online self-regulation in blended learning environments. Computers in Human Behavior, 99, 126-136. https://doi.org/10.1016/j.chb.2019.05.021
  • Vasu, K. A. P., Mei Fung, Y., Nimehchisalem, V., & Md Rashid, S. (2020). Self-Regulated Learning Development in Undergraduate ESL Writing Classrooms: Teacher Feedback Versus Self-Assessment. RELC Journal, 0033688220957782. https://doi.org/10.1177/0033688220957782
  • Wang, F. H. (2019). On the relationships between behaviors and achievement in technology-mediated flipped classrooms: A two-phase online behavioral PLS-SEM model. Computers & Education, 142, 103653. https://doi.org/10.1016/j.compedu.2019.103653
  • Winne, P. H. (1996). A metacognitive view of individual differences in self-regulated learning. Learning and Individual Differences, 8(4), 327-353. https://doi.org/10.1016/S1041-6080(96)90022-9
  • Wong, T. L., Xie, H., Zou, D., Wang, F. L., Tang, J. K. T., Kong, A., & Kwan, R. (2019). How to facilitate self-regulated learning? A case study on open educational resources. Journal of Computers in Education, 7(1), 51-77. https://doi.org/10.1007/s40692-019-00138-4
  • Yan, Z. (2020). Self-assessment in the process of self-regulated learning and its relationship with academic achievement. Assessment & Evaluation in Higher Education, 45(2), 224-238. https://doi.org/10.1080/02602938.2019.1629390
  • Zamora, Á., Suárez, J. M., & Ardura, D. (2018). Error detection and self-assessment as mechanisms to promote self-regulation of learning among secondary education students. The Journal of Educational Research, 111(2), 175-185. https://doi.org/10.1080/00220671.2016.1225657
  • Zhu, Y., Zhang, J. H., Au, W., & Yates, G. (2020). University students’ online learning attitudes and continuous intention to undertake online courses: a self-regulated learning perspective. Educational Technology Research and Development, 68, 1485–1519. https://doi.org/10.1007/s11423-020-09753-w
  • Zimmerman, B. J. (2002) Becoming a self-regulated learner: an overview. Theory Into Practice, 41(2), 64-70. https://doi.org/10.1207/s15430421tip4102_2