Personalized learning analytics intervention approach for enhancing student learning achievement and behavioral engagement in blended learning
Tóm tắt
The application of student interaction data is a promising field for blended learning (BL), which combines conventional face-to-face and online learning activities. However, the application of online learning technologies in BL settings is particularly challenging for students with lower self-regulatory abilities. In this study, a personalized learning analytics (LA) intervention approach that incorporates ebook and recommendation systems is proposed. The proposed approach provides students with actionable feedback regarding personalized remedial actions as the intervention to help them to strategically engage in the use of the ebook system and avoid academic failure when engaged in BL. A quasi-experiment was conducted to examine two classes of an undergraduate course that implemented a conventional BL model. The experimental group comprised 45 students from one class who learned using the proposed approach and received personalized intervention, whereas the control group comprised 42 students from the other class who learned using the conventional BL approach without receiving personalized intervention. The experimental results indicated that the proposed approach can improve students’ learning achievements and behavioral engagement in BL. The findings provide pedagogical insights into the application of LA intervention with actionable feedback in BL environments.
Tài liệu tham khảo
Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on Management of data (pp. 207–216).
Akçapınar, G., Hasnine, M. N., Majumdar, R., Flanagan, B., & Ogata, H. (2019). Developing an early-warning system for spotting at-risk students by using eBook interaction logs. Smart Learning Environments, 6(1), 1–15.
Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267–270). ACM.
Carless, D., & Boud, D. (2018). The development of student feedback literacy: Enabling uptake of feedback. Assessment & Evaluation in Higher Education, 43(8), 1315–1325. https://doi.org/10.1080/02602938.2018.1463354
Castro, R. (2019). Blended learning in higher education: Trends and capabilities. Education and Information Technologies, 24(4), 2523–2546.
Cheney, D., Flower, A., & Templeton, T. (2008). Applying response to intervention metrics in the social domain for students at risk of developing emotional or behavioral disorders. The Journal of Special Education, 42(2), 108–126.
Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98.
De Freitas, S., Gibson, D., Du Plessis, C., Halloran, P., Williams, E., Ambrose, M., & Arnab, S. (2015). Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology, 46(6), 1175–1188.
Deshpande, S., & Shesh, A. (2020). Performance assessment and remedies using blended learning for professional students. Computing in Engineering and Technology (pp. 783–790). Springer.
Du, M. C. (2004). Personalized annotation management for web based learning service. Master thesis. National Central University.
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.
Garrison, R., & Vaughan, N. (2008). Blended learning in higher education: Framework, principles and guidelines. Jossey-Bass.
Gong, L., Liu, Y., & Zhao, W. (2018). Using learning analytics to promote student engagement and achievement in blended learning: An empirical study. In Proceedings of the 2nd International Conference on E-Education, E-Business and E-Technology (pp. 19–24).
Graham, C. R., Woodfield, W., & Harrison, J. B. (2013). A framework for institutional adoption and implementation of blended learning in higher education. The Internet and Higher Education, 18, 4–14.
Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36–53.
Hu, S., & Kuh, G. D. (2002). Being (dis) engaged in educationally purposeful activities: The influences of student and institutional characteristics. Research in Higher Education, 43(5), 555–575.
Huang, C. S. J., Yang, S. J. H., Chiang, T. H. C., & Su, A. Y. S. (2016). Effects of Situated mobile learning approach on learning motivation and performance of EFL students. Educational Technology & Society, 19(1), 263–276.
Iraj, H., Fudge, A., Khan, H., Faulkner, M., Pardo, A., & Kovanovic, V. (2021). Narrowing the Feedback Gap: Examining Student Engagement with Personalized and Actionable Feedback Messages. Journal of Learning Analytics, 8(3), 101–116.
Jordan, K. (2014). Initial trends in enrolment and completion of massive open online courses. International Review of Research in Open and Distributed Learning, 15(1), 133–160.
Kazakoff, E. R., Macaruso, P., & Hook, P. (2018). Efficacy of a blended learning approach to elementary school reading instruction for students who are English learners. Educational Technology Research and Development, 66(2), 429–449.
Larrabee Sønderlund, A. L., Hughes, E., & Smith, J. (2019). The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology., 50(5), 2594–2618. https://doi.org/10.1111/bjet.12720
Lim, L. A., Gentili, S., Pardo, A., Kovanović, V., Whitelock-Wainwright, A., Gašević, D., & Dawson, S. (2021). What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course. Learning and Instruction, 72, 101202. https://doi.org/10.1016/j.learninstruc.2019.04.003
Liu, D. Y. T., Bartimote-Aufflick, K., Pardo, A., & Bridgeman, A. J. (2017). Data-driven personalization of student learning support in higher education. Learning analytics: Fundaments, applications, and trends (pp. 143–169). Springer.
Lu, O. H. T., Huang, J. C. H., Huang, A. Y. Q., & Yang, S. J. H. (2017). Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course. Interactive Learning Environments, 25(2), 220–234.
Lust, G., Elen, J., & Clarebout, G. (2013). Regulation of tool-use within a blended course: Student differences and performance effects. Computers & Education, 60(1), 385–395.
McMaster, K. L., Kunkel, A., Shin, J., Jung, P. G., & Lembke, E. (2018). Early writing intervention: A best evidence synthesis. Journal of Learning Disabilities, 51(4), 363–380.
McNicholas, P. D., Murphy, T. B., & O’Regan, M. (2008). Standardising the lift of an association rule. Computational Statistics & Data Analysis, 52(10), 4712–4721.
Na, K. S., & Tasir, Z. (2017). A systematic review of learning analytics intervention contributing to student success in online learning. In 2017 International conference on learning and teaching in computing and engineering (LaTICE) (pp. 62–68). IEEE.
Ogata, H., Yin, C., Oi, M., Okubo, F., Shimada, A., Kojima, K., & Yamada, M. (2015). E-Book-based learning analytics in university education. International conference on computer in education (ICCE 2015) (pp. 401–406). Asia-Pacifc Society for Computers in Education.
Owston, R., York, D., & Murtha, S. (2013). Student perceptions and achievement in a university blended learning strategic initiative. The Internet and Higher Education, 18, 38–46.
Rumberger, R. W., & Rotermund, S. (2012). The relationship between engagement and high school dropout. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 491–513). Springer.
Sacr, M., Fors, U., Tedre, M., & Nouri, J. (2018). How social network analysis can be used to monitor online collaborative learning and guide an informed intervention. PLoS ONE, 13(3), e0194777.
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31–40.
Siemens, G. (2012). Learning analytics: envisioning a research discipline and a domain of practice. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 4–8). ACM.
Wise, A. F. (2014). Designing pedagogical interventions to support student use of learning analytics. In Proceedings of the 4th International Conference on Learning Analytics and Knowledge (pp. 203–211). ACM.
Wong, B. T. M., & Li, K. C. (2020). A review of learning analytics intervention in higher education (2011–2018). Journal of Computers in Education, 7(1), 7–28.
Yang, C. C., Chen, I. Y., Akçapınar, G., Flanagan, B., & Ogata, H. (2021a). Using a summarized lecture material recommendation system to enhance students’ preclass preparation in a flipped classroom. Educational Technology & Society, 24(2), 1–13.
Yang, C. C., Chen, I. Y., & Ogata, H. (2021b). Toward precision education: educational data mining andlearning analytics for identifying students’ learning patterns with ebook systems. Educational Technology & Society, 24(1), 152–163.
Yang, C. C., & Ogata, H. (2022). Personalized review learning approach for improving behavioral engagement and academic achievement in language learning through e-books. Education and Information Technologies, 1–18.
Yeh, S. W., & Lo, J. J. (2009). Using online annotations to support error correction and corrective feedback. Computers & Education, 52(4), 882–892.
Zhang, Y., Fei, Q., Quddus, M., & Davis, C. (2014). An Examination of the impact of early intervention on learning outcomes of at-risk students. Research in Higher Education Journal, 26, 1–12.
Zhang, J.-H., Zhang, Y.-X., Zou, Q., & Huang, S. (2018). What learning analytics tells Us: Group behavior analysis and indi- vidual learning diagnosis based on long-term and large-scale data. Educational Technology & Society, 21(2), 245–258.
Zhang, J. H., Zou, L. C., Miao, J. J., Zhang, Y. X., Hwang, G. J., & Zhu, Y. (2020). An individualized intervention approach to improving university students’ learning performance and interactive behaviors in a blended learning environment. Interactive Learning Environments, 28(2), 231–245.
Zhou, M., & Winne, P. H. (2012). Modeling academic achievement by self-reported versus traced goal orientation. Learning and Instruction, 22(6), 413–419.