FLoRA Facilitating Self-Regulated Learning with Personalized Scaffolds on Student’s own Regulation Activities
Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
Developmental and Educational Psychology
Final Report Abstract
We investigated three important research questions with high scientific impact. First, we developed a way to capture learning behavior in an accurate and meaningful way via log data. This means that we can slowly move from think aloud studies, which are obtrusive and timeconsuming, to log data studies, which are unobtrusive and less time-consuming. To that end, we developed and validated an analytics-based self-regulated learning measurement protocol and, at the same time, enhanced the granularity of trace data collected during students’ learning. A rule-based artificial intelligence system was developed and used for capturing and supporting learning in real-time. Second, we developed scaffolds that were personalized to learners’ own self-regulated learning process, which was not done before. Third, we developed personalized scaffolds and, by doing so, introduced an approach to designing such scaffolds to support learning. In other words, we described our design process and decisions made along the way to inform future design of personalized scaffolds. Furthermore, we tested and refined personalized scaffolds to improve how students complied with them. The sophisticated infrastructure developed with learning tools, personalized scaffolds, a learning environment, and a rule-based artificial system embedded and integrated served as a basis as well as an exemplar for future advanced learning technologies to build upon. The largest obstacle was the COVID-19 pandemic. We ensured safe data collection by adhering to local policies. Regarding the results, one of the "surprises" was that the CG students in Study 3 who did not receive scaffolds learned similarly to the EG students who received scaffolds, i.e., they also showed more regulatory events and similar learning patterns, especially in relation to the students in Study 1 (who also studied without scaffolds).We interpret these results to mean that the use of an improved learning environment in Study 3 through the addition of instrumentation tools (e.g., planners, notes, etc.) also led to improved learning regulation.
Publications
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Do Instrumentation Tools Capture Self-Regulated Learning?. LAK21: 11th International Learning Analytics and Knowledge Conference, 438-448. ACM.
van der Graaf, Joep; Lim, Lyn; Fan, Yizhou; Kilgour, Jonathan; Moore, Johanna; Bannert, Maria; Gasevic, Dragan & Molenaar, Inge
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Temporal Assessment of Self-Regulated Learning by Mining Students’ Think-Aloud Protocols. Frontiers in Psychology, 12.
Lim, Lyn; Bannert, Maria; van der Graaf, Joep; Molenaar, Inge; Fan, Yizhou; Kilgour, Jonathan; Moore, Johanna & Gašević, Dragan
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Improving the measurement of self-regulated learning using multi-channel data. Metacognition and Learning, 17(3), 1025-1055.
Fan, Yizhou; Lim, Lyn; van der Graaf, Joep; Kilgour, Jonathan; Raković, Mladen; Moore, Johanna; Molenaar, Inge; Bannert, Maria & Gašević, Dragan
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The Dynamics Between Self-Regulated Learning and Learning Outcomes: an Exploratory Approach and Implications. Metacognition and Learning, 17(3), 745-771.
van der Graaf, Joep; Lim, Lyn; Fan, Yizhou; Kilgour, Jonathan; Moore, Johanna; Gašević, Dragan; Bannert, Maria & Molenaar, Inge
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Towards investigating the validity of measurement of self-regulated learning based on trace data. Metacognition and Learning, 17(3), 949-987.
Fan, Yizhou; van der Graaf, Joep; Lim, Lyn; Raković, Mladen; Singh, Shaveen; Kilgour, Jonathan; Moore, Johanna; Molenaar, Inge; Bannert, Maria & Gašević, Dragan
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Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning. Computers in Human Behavior, 139, 107547.
Lim, Lyn; Bannert, Maria; van der Graaf, Joep; Singh, Shaveen; Fan, Yizhou; Surendrannair, Surya; Rakovic, Mladen; Molenaar, Inge; Moore, Johanna & Gašević, Dragan
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How do students learn with real‐time personalized scaffolds?. British Journal of Educational Technology, 55(4), 1309-1327.
Lim, Lyn; Bannert, Maria; van der Graaf, Joep; Fan, Yizhou; Rakovic, Mladen; Singh, Shaveen; Molenaar, Inge & Gašević, Dragan
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How to design and evaluate personalized scaffolds for self-regulated learning. Metacognition and Learning, 18(3), 783-810.
van der Graaf, Joep; Raković, Mladen; Fan, Yizhou; Lim, Lyn; Singh, Shaveen; Bannert, Maria; Gašević, Dragan & Molenaar, Inge
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Towards a fuller picture: Triangulation and integration of the measurement of self‐regulated learning based on trace and think aloud data. Journal of Computer Assisted Learning, 39(4), 1303-1324.
Fan, Yizhou; Rakovic, Mladen; van der Graaf, Joep; Lim, Lyn; Singh, Shaveen; Moore, Johanna; Molenaar, Inge; Bannert, Maria & Gašević, Dragan
