Exploring Primary School Students’ Self-Regulated Learning Profiles in a Web-Based Inquiry Science Environment
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11165-024-10159-4.pdf
Reference44 articles.
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2. Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-Behnagh, R., Bouchet, F., & Landis, R. (2013). Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies, Vol. 28. Springer, 427-449
3. Baars, M., & Wijnia, L. (2018). The relation between task-specific motivational profiles and training of self-regulated learning skills. Learning and Individual Differences, 64(September 2016), 125–137. https://doi.org/10.1016/j.lindif.2018.05.007
4. Biswas, G., Kinnebrew, J. S., Segedy, J. R. (2014). Using a cognitive/metacognitive task model to analyze students learning behaviors. In D. D. Schmorrow, C. M. Fidopiastis (Eds), Foundations of augmented cognition. Advancing human performance and decision-making through adaptive systems, vol. 8534. Springer International Publishing, 190–201. https://doi.org/10.1007/978-3-319-07527-3_18
5. Bouchet, F., Harley, J. M., Trevors, G. J., & Azevedo, R. (2013). Clustering and profiling students according to their interactions with an intelligent tutoring system fostering self-regulated learning. Journal of Educational Data Mining, 5(1), 104–146. https://doi.org/10.5281/zenodo.3554613
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