ECTEL 22 : SELF-REGULATED LEARNING
Workshop on Improving the Instrumentation
and Feedback for Self-Regulated Learning
SEPTEMBER 13, 2022
9:00 am - 3:30 pm (CET)
MODE: FACE-TO-FACE
Room A203 ( ENSEEIHT, 2 Rue Charles Camichel, 31000 Toulouse )
Workshop on Improving the Instrumentation
and Feedback for Self-Regulated Learning
SEPTEMBER 13, 2022
9:00 am - 3:30 pm (CET)
MODE: FACE-TO-FACE
Room A203 ( ENSEEIHT, 2 Rue Charles Camichel, 31000 Toulouse )
BACKGROUND
One of the central focuses of education is to foster the competency of Self-Regulated Learning (SRL) amongst learners. Self-Regulation improves learning outcomes revealed by the positive relation between SRL processes and measures of learning (Harley, Taub, Azevedo & Bouchet, 2017; Bannert, 2007; Bannert et al., 2014; Rakovic et al., 2022), as well as increases the learner's ability to engage in lifelong learning (Klug et al., 2011; Schunk and Greene, 2018).
Measuring SRL, however, has posed a major challenge to researchers for decades (van der Graaf et al., 2021). Methods commonly used to measure SRL processes to date have included self-report surveys (Pintrich & et al. 1991), think-aloud protocols (Bannert, 2007; Azevedo et al., 2005; Greene et al., 2008), and more recently the use of unobtrusive trace-based measurement (Kinnebrew et al., 2014; Siadaty et al., 2016; Fan et al., 2021). However, these approaches posit their own challenges and limitations - from the cumbersome transcription and coding process of interviews and think-aloud sessions, to the lack of clear and consistent representation of learning actions in the log data (Bråten and Samuelstuen, 2007; Greene and Azevedo, 2009; Bernacki. 2018).
Instrumentation tools, embedded within learning platforms, are proposed as a promising way to improve the capture of SRL processes that are otherwise hard to detect (Marzouk et al, 2016, Winnie 2017; van der Graaf, 2021). However, tool adoption in itself has its issues, such as the learners being aware of the tool, whether they think the tool can be useful for the given task, and whether students have sufficient skills to use it (Winnie, 2006; van der Graaf, 2021).
Considering the many challenges that still exist with the detection, measurement, and validation of SRL processes, this workshop provides a forum for interested researchers to examine the current SRL-related work-to-date, and explore how they can build upon existing methods of instrumentation, measurement, analytics and feedback related to SRL.
The workshop is planned to be a full-day event. In the morning session (Part 1), participants will have the opportunity to present their own research and practice projects on SRL and discuss how learning analytics can contribute towards the improvement of data collection, analysis and feedback related to SRL. They will also have a hands-on opportunity to explore a learning analytics platform (FLoRA) integrated with various instrumentation tools and the participants will be able to explore their own data as they participate in a learning activity.
In the second part of the workshop, participants will have the opportunity to explore a synthetic dataset, containing multi-channel trace-based data measuring learners’ metacognitive processes. In small groups, the participants will have the opportunity to analyse the data, make sense of it, and co-design possible SRL related scaffolds and feedback representations for learners and instructors. In this way, the workshop will provide opportunities, not only to learn about exciting new tools and methods, but also to allow the participants to share their own ideas and research projects, and meet and collaborate with other researchers in this area.
References ▼
Azevedo, R., Cromley, J. G., Winters, F. I., Moos, D. C., & Greene, J. A. (2005). Adaptive human scaffolding facilitates adolescents’ self-regulated learning with hypermedia. Instructional science, 33(5), 381-412.
Bannert, M. (2007). Metakognition beim lernen mit hypermedien. Waxmann Verlag.
Bannert, M., Reimann, P., & Sonnenberg, C. (2014). Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacognition and learning, 9(2), 161-185.
Bråten, I., & Samuelstuen, M. S. (2007). Measuring strategic processing: Comparing task-specific self-reports to traces. Metacognition and Learning, 2(1), 1-20.
Fan, Y., van der Graaf, J., Lim, L., Raković, M., Singh, S., Kilgour, J., Moore, J., Molenaar, I., Bannert, M., & Gašević, D. Towards investigating the validity of measurement of self-regulated learning based on trace data. Metacognition Learning (2022).
Greene, J. A., & Azevedo, R. (2009). A macro-level analysis of SRL processes and their relations to the acquisition of a sophisticated mental model of a complex system. Contemporary educational psychology, 34(1), 18-29.
Harley, J. M., Taub, M., Azevedo, R., & Bouchet, F. (2017). Let's set up some subgoals: Understanding human-pedagogical agent collaborations and their implications for learning and prompt and feedback compliance. IEEE Transactions on Learning Technologies, 11(1), 54-66.
Kinnebrew, J. S., Segedy, J. R., & Biswas, G. (2014). Analyzing the temporal evolution of students’ behaviors in open-ended learning environments. Metacognition and learning, 9(2), 187-215.
Marzouk, Z., Rakovic, M., Liaqat, A., Vytasek, J., Samadi, D., Stewart-Alonso, J., I., Woloshen, S., Winne, P.H.& Nesbit, J. C. (2016). What if learning analytics were based on learning science?. Australasian Journal of Educational Technology, 32(6).
Pintrich, P. R. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ).
Raković, M., Fan, Y., van der Graaf, J., Singh, S., Kilgour, J., Lim, L., Moore, J., Bannert, M., Molenaar, I. and Gašević, D. (2022, March). Using Learner Trace Data to Understand Metacognitive Processes in Writing from Multiple Sources In Proceedings of the 12th International Conference on Learning Analytics & Knowledge (pp. 130-141).
Siadaty, M., Gasevic, D., & Hatala, M. (2016). Trace-based micro-analytic measurement of self-regulated learning processes. Journal of Learning Analytics, 3(1), 183-214.
van der Graaf, J., Molenaar, Lim L., I., Fan, Y.,Kilgour, J., Moore, J., Gasevic, D. & Bannert, M. (2021, April). Do Instrumentation Tools Capture Self-Regulated Learning? In Proceedings of the 11th International Conference on Learning Analytics & Knowledge (pp. 438-448).
Winne, P. H. (2017). Learning analytics for self-regulated learning. Handbook of learning analytics, 241-249.