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 )

OVERVIEW

One of the central focuses of education is to foster the competency of Self-Regulated Learning (SRL) amongst learners. However, measuring SRL has posed a major challenge to researchers for decades. 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 hands-on event. In 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. In Part 2, the focus will be on sharing insights on scaffold design and examining types of SRL support that are effective in different learning contexts.

MOTIVATION

Measuring SRL has posed a major challenge to researchers for decades (van der Graaf et al., 2021). Various instrumentation tools have been proposed to help improve the capture SRL processes, ranging from self-reported questionnaires (Bråten and Samuelstuen, 2007), structured interviews and think-aloud (Greene and Azevedo, 2009; Lim et al., 2021), and more recently through unobtrusive trace data collection (Siadaty, Gašević, and Hatala, 2016; Fan et al., 2022). However, the detection, measurement, and validation of SRL processes with these data channels is still a much-debated issue within the SRL community (Bernacki, 2018; Winne, 2020).

Trace-based methods are becoming a popular approach to measuring SRL (Winne, 2010; Siadaty et al., 2016). Trace data can unobtrusively record instances of cognition and metacognition in authentic learning environments and thus operationalize “what learners do as they do it” (Winne, 2010) and has been utilised in a number of studies (Siadaty et al., 2016; Saint et al., 2020, 2021; Fan et al., 2021, Srivastava et al, 2022).

In this workshop, participants with an interest in the measurement and analysis of SRL will have the opportunity to come together and explore how tools and data channels can be combined to improve the measurement of SRL. The workshop also provides a hands-on opportunity to examine existing learning analytics platforms and trace-based datasets, and explore ways how they can be effectively utilised to support teaching and learning.

Places are limited.

To Register: Register for ECTEL 2022: Workshop on SRL

View our Call for Participation.

References ▼

  • Bernacki, M. L. (2017). Examining the cyclical, loosely sequenced, and contingent features of self-regulated learning: Trace data and their analysis. In Handbook of self-regulation of learning and performance (pp. 370-387). Routledge.

  • 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., ... & Gašević, D. (2022). Towards investigating the validity of measurement of self-regulated learning based on trace data. Metacognition and Learning, 1-39.

  • 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.

  • Lim, L., Bannert, M., van der Graaf, J., Molenaar, I., Fan, Y., Kilgour, J., Moore, J. and Gašević, D. (2021). Temporal Assessment of Self-Regulated Learning by Mining Students’ Think-Aloud Protocols. Frontiers in Psychology, 12.

  • Saint, J., Fan, Y., Singh, S., Gasevic, D., & Pardo, A. (2021, April). Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms. In LAK21: 11th International Learning Analytics and Knowledge Conference (pp. 333-343).

  • 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.

  • Srivastava, N., Fan, Y., Rakovic, M., Singh, S., Jovanovic, J., van der Graaf, J., ... & Gasevic, D. (2022, March). Effects of Internal and External Conditions on Strategies of Self-regulated Learning: A Learning Analytics Study. In LAK22: 12th International Learning Analytics and Knowledge Conference (pp. 392-403).

  • Van Der Graaf, J., Lim, L., Fan, Y., Kilgour, J., Moore, J., Bannert, M., ... & Molenaar, I. (2021, April). Do instrumentation tools capture self-regulated learning?. In LAK21: 11th International Learning Analytics and Knowledge Conference (pp. 438-448).

  • Winne, P. H. (2020). Construct and consequential validity for learning analytics based on trace data. Computers in Human Behavior, 112, 106457.