The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Improving Measurements of Self-Regulated Learning
Abstract
Articles in this special issue present recent advances in using state-of-the-art software systems that gather data with which to examine and measure features of learning and particularly self-regulated learning (SRL). Despite important advances, there remain challenges. I examine key features of SRL and how they are measured using common tools. I advance the case that traces of cognition and metacognition offer critical information about SRL that other state-of-the-art measurements cannot.
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Citations
Cites
Studying as Self-Regulated Learning
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“CBLEs are splendid tools for gathering extensive data about many factors in the learner’s environment that may shape SRL.”
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Kornell, N., & Bjork, R. A. (2007) · Psychonomic Bulletin & Review · doi:10.3758/bf03194055 · Cited by 527
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Minimizing the black box problem to enhance the validity of theories about instructional effects.
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Self-citation
Cited by
Mega, C., Ronconi, L., & De Beni, R. (2014) · Journal of Educational Psychology · doi:10.1037/a0033546 · Cited by 1,007
Comparing online and blended learner's self-regulated learning strategies and academic performance
Broadbent, J. (2017) · The Internet and Higher Education · doi:10.1016/j.iheduc.2017.01.004 · Cited by 679
ChatGPT improves creative problem-solving performance in university students: An experimental study
Urban, M., Děchtěrenko, F., Lukavský, J., Hrabalová, V., Svacha, F., Brom, C., & Urban, K. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105031 · Cited by 396
Jansen, R. S., van Leeuwen, A., Janssen, J., Jak, S., & Kester, L. (2019) · Educational Research Review · doi:10.1016/j.edurev.2019.100292 · Cited by 310
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Cognitive Load as Motivational Cost
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Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning
Lim, L., Bannert, M., van der Graaf, J., Singh, S., Fan, Y., Surendrannair, S., Rakovic, M., Molenaar, I., Moore, J., & Gašević, D. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107547 · Cited by 198
What multimodal data can tell us about the students’ regulation of their learning process?
Järvelä, S., Malmberg, J., Haataja, E., Sobocinski, M., & Kirschner, P. A. (2021) · Learning and Instruction · doi:10.1016/j.learninstruc.2019.04.004 · Cited by 163
Multimodal data to design visual learning analytics for understanding regulation of learning
Noroozi, O., Alikhani, I., Järvelä, S., Kirschner, P. A., Juuso, I., & Seppänen, T. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.12.019 · Cited by 140
Analytics of time management strategies in a flipped classroom
Ahmad Uzir, N., Gašević, D., Matcha, W., Jovanović, J., & Pardo, A. (2020) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12392 · Cited by 129
Which one works best? Considering the relative importance of motivational regulation strategies
Schwinger, M., & Otterpohl, N. (2017) · Learning and Individual Differences · doi:10.1016/j.lindif.2016.12.003 · Cited by 111
Chou, C.-Y., & Zou, N.-B. (2020) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-020-00233-y · Cited by 102
Towards investigating the validity of measurement of self-regulated learning based on trace data
Fan, Y., van der Graaf, J., Lim, L., Raković, M., Singh, S., Kilgour, J., Moore, J., Molenaar, I., Bannert, M., & Gašević, D. (2022) · Metacognition and Learning · doi:10.1007/s11409-022-09291-1 · Cited by 101
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Learning Strategies, Study Skills, and Self-Regulated Learning in Postsecondary Education
Winne, P. H. (2013) · Higher Education: Handbook of Theory and Research · doi:10.1007/978-94-007-5836-0_8 · Cited by 73
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Using trace data to enhance Students' self-regulation: A learning analytics perspective
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How do students learn with real-time personalized scaffolds?
Lim, L., Bannert, M., van der Graaf, J., Fan, Y., Rakovic, M., Singh, S., Molenaar, I., & Gašević, D. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13414 · Cited by 66
What aspects of online peer feedback robustly predict growth in students' task performance?
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How does self-regulated learning relate to active procrastination and other learning behaviors?
Yamada, M., Goda, Y., Matsuda, T., Saito, Y., Kato, H., & Miyagawa, H. (2016) · Journal of Computing in Higher Education · doi:10.1007/s12528-016-9118-9 · Cited by 50
Reliability and Validity Analyses of a Newly Developed Test to Assess Learning Strategy Knowledge
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Temporal Assessment of Self-Regulated Learning by Mining Students’ Think-Aloud Protocols
Lim, L., Bannert, M., van der Graaf, J., Molenaar, I., Fan, Y., Kilgour, J., Moore, J., & Gašević, D. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.749749 · Cited by 44
Desai, S., & Chin, J. (2020) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181320641112 · Cited by 9
Metadata
- Authors
- WINNE, P. H.
- Venue
- Educational Psychologist
- Publisher
- Informa UK Limited
- Published
- 2010-10-25
- Volume
- 45
- Issue
- 4
- Pages
- 267-276
- ISSN
- 0046-1520 (ISSN-L)
- 1532-6985
- OpenAlex
- W2129062063
- Cited by
- 405
- References
- 7
- FWCI
- 37.85
- Type
- journal article
- Language
- en
- Open access
- closed
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