Abstract
Flipping the classroom requires from students some self-regulated learning skills, as they must have engaged in learning activities prior to attending classes. The study we describe in this paper was done in the context of a 15-week flipped course delivered online to a large class of undergraduate students. We collected various time-stamped digital traces generated by the students’ engagement in the required weekly learning activities (H5P interactive videos, quizzes and worksheets). The collected data allowed the generation of visual learning pathways, from which several types of learning profiles emerged. A distance measure between the students’ learning pathways and the instructor’s recommended pathway was found to be negatively correlated with exam performance. The results from a survey collecting students’ perceptions of their engagement with the learning activities are also presented.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zimmerman, B.J.: A social cognitive view of self-regulated academic learning. J. Educ. Psychol. 81, 329–339 (1989)
Broadbent, J., Poon, W.L.: Self-regulated learning strategies and academic achievement in online higher education learning environments: a systematic review. Internet High. Educ. 27, 1–13 (2015)
Van Rooij, S.W., Zirkle, K.: Balancing pedagogy, student readiness and accessibility: a case study in collaborative online course development. Internet High. Educ. 28, 1–7 (2016)
Srivastava, N., Fan, Y., Rakovic, M., et al.: Effects of internal and external conditions on strategies of self-regulated learning: a learning analytics Study. In: 12th International Learning Analytics and Knowledge Conference (LAK22), pp. 392–403. ACM (2022)
Zhang, T., Taub, M., Chen, Z.: A multi-level trace clustering analysis scheme for measuring students’ self-regulated learning behavior in a mastery-based online learning environment. In: 12th International Learning Analytics and Knowledge Conference (LAK22), pp. 197–207. ACM (2022)
Lahza, H., Khosravi, H., Demartini, G., Gasevic, D.: Effects of technological interventions for self-regulation: a control experiment in learnersourcing. In: 12th International Learning Analytics and Knowledge Conference (LAK22), ACM, pp. 542–548 (2022)
Kim, D., Yoon, M., Jo, I., Branch, R.M.: Learning analytics to support self-regulated learning in asynchronous online courses: a case study at a women’s university in South Korea. Comput. Educ. 127, 233–251 (2018)
Effeney, G., Carroll, A., Bahr, N.: Self-regulated learning: key strategies and their sources in a sample of adolescent males. Aust. J. Educ. Dev. Psychol. 13, 58–74 (2013)
Alario-Hoyos, C., Pérez-Sanagustín, M., Delgado-Kloos, C., Parada G, H.A., Muñoz-Organero, M.: Delving into participants’ profiles and use of social tools in MOOCs. IEEE Trans. Learn. Technol. 7(3), 260–266 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bourguet, ML. (2022). Measuring Learners’ Self-regulated Learning Skills from Their Digital Traces and Learning Pathways. In: Hilliger, I., Muñoz-Merino, P.J., De Laet, T., Ortega-Arranz, A., Farrell, T. (eds) Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption. EC-TEL 2022. Lecture Notes in Computer Science, vol 13450. Springer, Cham. https://doi.org/10.1007/978-3-031-16290-9_42
Download citation
DOI: https://doi.org/10.1007/978-3-031-16290-9_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16289-3
Online ISBN: 978-3-031-16290-9
eBook Packages: Computer ScienceComputer Science (R0)