Abstract
Self-regulated learning (SRL) is a critical skill for learners to acquire, and academics have designed and implemented SRL scaffolding to support learners in developing their SRL and use of learning strategies. Adaptive scaffolding is believed to be more effective in promoting SRL, but limited studies have explored how different learners perceive the adaptivity of scaffolding to their personal needs in relation to the strategies they use. This study recruited 22 undergraduate learners who were given an online learning task and provided with adaptive scaffolding. Post-task interviews were conducted to understand their learning strategies and how they perceived the adaptivity of the scaffolds. We used epistemic network analysis (ENA) to understand the associations among learning strategies and perceived adaptivity, and to examine whether these associations differed according to task performance. Results indicate that learners’ adoption of learning strategies is associated with their perceived adaptivity of scaffolding, and the association differed between high- and low-performing learners. Learners who adopted a strategy consistent with the scaffolding design expressed a stronger appreciation of its adaptivity and demonstrated higher task performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aleven, V., McLaughlin, E.A., Glenn, R.A., Koedinger, K.R.: Instruction based on adaptive learning technologies. Handb. Res. Learn. Instruct. 2, 522–560 (2016)
Azevedo, R., Cromley, J.G., Moos, D.C., Greene, J.A., Winters, F.I.: Adaptive content and process scaffolding: a key to facilitating students’ self-regulated learning with hypermedia. Psychol. Test Assess. Model. 53(1), 106 (2011)
Bannert, M., Reimann, P.: Supporting self-regulated hypermedia learning through prompts. Instr. Sci. 40, 193–211 (2012)
Bannert, M., Sonnenberg, C., Mengelkamp, C., Pieger, E.: Short-and long-term effects of students’ self-directed metacognitive prompts on navigation behavior and learning performance. Comput. Hum. Behav. 52, 293–306 (2015)
Broadbent, J.: Comparing online and blended learner’s self-regulated learning strategies and academic performance. Internet High. Educ. 33, 24–32 (2017)
Duffy, M.C., Azevedo, R.: Motivation matters: interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Comput. Hum. Behav. 52, 338–348 (2015)
Greene, J.A., Azevedo, R.: A theoretical review of Winne and Hadwin’s model of self-regulated learning: new perspectives and directions. Rev. Educ. Res. 77(3), 334–372 (2007)
Hadwin, A.F., Winne, P.H., Stockley, D.B., Nesbit, J.C., Woszczyna, C.: Context moderates students’ self-reports about how they study. J. Educ. Psychol. 93(3), 477 (2001)
Jansen, R.S., van Leeuwen, A., Janssen, J., Conijn, R., Kester, L.: Supporting learners’ self-regulated learning in massive open online courses. Comput. Educ. 146, 103771 (2020)
Jivet, I., Scheffel, M., Specht, M., Drachsler, H.: License to evaluate: preparing learning analytics dashboards for educational practice. In: Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pp. 31–40 (2018)
Jovanović, J., Gašević, D., Pardo, A., Dawson, S., Whitelock-Wainwright, A.: Introducing meaning to clicks: towards traced-measures of self-efficacy and cognitive load. In: Proceedings of the 9th International Conference on Learning Analytics & Knowledge, pp. 511–520 (2019)
Lim, L.A., et al.: Students’ sense-making of personalised feedback based on learning analytics. Australas. J. Educ. Technol. 36(6), 15–33 (2020)
Lim, L.A., et al.: Students’ perceptions of, and emotional responses to, personalised learning analytics-based feedback: an exploratory study of four courses. Assess. Eval. High. Educ. 46(3), 339–359 (2021)
Lim, L., et al.: Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning. Comput. Hum. Behav. 139, 107547 (2023)
Milikić, N., Gašević, D., Jovanović, J.: Measuring effects of technology-enabled mirroring scaffolds on self-regulated learning. IEEE Trans. Learn. Technol. 13(1), 150–163 (2018)
Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., Mirriahi, N.: Using learning analytics to scale the provision of personalised feedback. Br. J. Edu. Technol. 50(1), 128–138 (2019)
Pea, R.D.: The social and technological dimensions of scaffolding and related theoretical concepts for learning, education, and human activity. J. Learn. Sci. 423–451 (2018)
Pieger, E., Bannert, M.: Differential effects of students’ self-directed metacognitive prompts. Comput. Hum. Behav. 86, 165–173 (2018)
Rakovic, M., et al.: Using learner trace data to understand metacognitive processes in writing from multiple sources. In: LAK22: 12th International Learning Analytics and Knowledge Conference, pp. 130–141 (2022)
Shaffer, D.W., Ruis, A.R.: How we code. In: Ruis, A.R., Lee, S.B. (eds.) ICQE 2021. CCIS, vol. 1312, pp. 62–77. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67788-6_5
Siadaty, M., Gašević, D., Hatala, M.: Associations between technological scaffolding and micro-level processes of self-regulated learning: a workplace study. Comput. Hum. Behav. 55, 1007–1019 (2016)
Sitzmann, T., Bell, B.S., Kraiger, K., Kanar, A.M.: A multilevel analysis of the effect of prompting self-regulation in technology-delivered instruction. Pers. Psychol. 62(4), 697–734 (2009)
Sonnenberg, C., Bannert, M.: Discovering the effects of metacognitive prompts on the sequential structure of SRL-processes using process mining techniques. J. Learn. Anal. 2(1), 72–100 (2015)
Weinstein, C.E., Mayer, R.E.: The teaching of learning strategies. In: Innovation abstracts, vol. 5, p. n32. ERIC (1983)
Winne, P.H.: Experimenting to bootstrap self-regulated learning. J. Educ. Psychol. 89(3), 397 (1997)
Winne, P.H.: How software technologies can improve research on learning and bolster school reform. Educ. Psychol. 41(1), 5–17 (2006)
Winne, P.H.: Learning strategies, study skills, and self-regulated learning in postsecondary education. High. Educ.: Handb. Theory Res. 28, 377–403 (2013)
Zhou, M., Winne, P.H.: Modeling academic achievement by self-reported versus traced goal orientation. Learn. Instr. 22(6), 413–419 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, T. et al. (2023). Do Learners Appreciate Adaptivity? An Epistemic Network Analysis of How Learners Perceive Adaptive Scaffolding. In: Arastoopour Irgens, G., Knight, S. (eds) Advances in Quantitative Ethnography. ICQE 2023. Communications in Computer and Information Science, vol 1895. Springer, Cham. https://doi.org/10.1007/978-3-031-47014-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-47014-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47013-4
Online ISBN: 978-3-031-47014-1
eBook Packages: Computer ScienceComputer Science (R0)