LAK23 Trace-SRL
Workshop on Measuring and Facilitating
Self-regulated Learning based on Trace data
DATE: March 14th , 2023
TIME: 9:00am - 5:00pm
MODE: FACE-TO-FACE
VENUE: Arlington, Texas, USA.
Workshop on Measuring and Facilitating
Self-regulated Learning based on Trace data
DATE: March 14th , 2023
TIME: 9:00am - 5:00pm
MODE: FACE-TO-FACE
VENUE: Arlington, Texas, USA.
BACKGROUND
One of the central focuses of education is to foster the competency of Self-Regulated Learning (SRL) amongst learners. Self-regulation can improve learning outcomes as revealed by the positive relation between SRL processes and measures of learning (Azevedo et al., 2022). Self-regulation can also increase learners’ ability to engage in life-long learning (Klug et al., 2011). Measuring SRL, however, has posed a major challenge to researchers for decades. Various measurement tools and methods have been proposed to help improve the capture of SRL processes, ranging from self-report surveys (Pintrich & et al. 1991) to think-aloud protocols (Bannert, 2007) and trace-based measurement (Siadaty et al., 2016; Fan et al., 2022). Trace-based methods are becoming a popular approach to measuring SRL (Saint et al., 2022), since trace data can unobtrusively record dynamic 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; Fan et al., 2022). However, the detection, measurement, and validation of SRL processes with trace data is still a much debated issue within the SRL community (Winne, 2020). Therefore, we would like to propose this interactive workshop (aim 1) for interested interdisciplinary researchers from different learning analytic projects focused on SRL to examine current SRL-related work-to-date, explore how they can build upon existing methods of measurement of SRL, and exchange their lessons learnt from different projects.
While the importance of SRL to learning is widely recognised, numerous previous studies have also shown that learners by themselves often experience difficulties in adequately and effectively self-regulating their learning across tasks, domains, and contexts (Azevedo et a., 2010; Winne, 2010). Despite the opportunities learners have to practice and hone them, SRL skills remain underdeveloped (Bjork et., 2013). Therefore, learners need to be supported to successfully regulate their learning and achieve their learning goals. Different types of interventions, such as scaffolding, dashboards, or personalized feedback, have been designed in learning analytics to effectively support learners’ SRL and ultimately improve their SRL skills. However, there is limited research into the development of these interventions and how design decisions are associated with the execution of SRL and learning outcomes (Devolder et al., 2012). Importantly, the complex conditions and contexts when these interventions facilitate and enhance learning are emerging (Guo, 2022). Therefore, this interactive workshop (aim 2) will address these challenges by sharing how different interventions (e.g., artificial agents) can be designed, the potential of the interventions, and/or how effective interventions are in supporting SRL. This will lead to new insights concerning the effectiveness of intervention approaches to facilitate metacognition and self-regulation during learning.
References ▼
Azevedo, R., Moos, D. C., Johnson, A. M., & Chauncey, A. D. (2010). Measuring cognitive and metacognitiveregulatory processes during hypermedia learning: Issues and challenges. Educationalpsychologist, 45(4), 210-223.
Azevedo, R., Bouchet, F., Duffy, M., Harley, J., Taub, M., Trevors, G., ... & Cerezo, R. (2022). Lessonslearned and future directions of metatutor: leveraging multichannel data to scaffold self-regulatedlearning with an intelligent tutoring system. Frontiers in Psychology, 13.
Bannert, M. (2007). Metakognition beim lernen mit hypermedien. Waxmann Verlag.
Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: Beliefs, techniques, and illusions. Annual review of psychology, 64, 417-444.
Devolder, A., van Braak, J., & Tondeur, J. (2012). Supporting self‐regulated learning in computer based learning environments: systematic review of effects of scaffolding in the domain of science education. Journal of Computer Assisted Learning, 28(6), 557-573.
Fan, Y., van der Graaf, J., Lim, L., Raković, M., Singh, S., Kilgour, J., ... & Gašević, D. (2022). Towardsinvestigating the validity of measurement of self-regulated learning based on trace data. Metacognitionvand Learning, 1-39.
Guo, L. (2022). Using metacognitive prompts to enhance self‐regulated learning and learning outcomes:A meta‐analysis of experimental studies in computer‐based learning environments.Journal of Computer Assisted Learning, 38(3), 811-832.
Klug, J., Ogrin, S., Keller, S., Ihringer, A., & Schmitz, B. (2011). A plea for self-regulated learning as a process: Modelling, measuring and intervening. Psychological Test and Assessment Modeling,53(1), 51.
Pintrich, P. R., & De Groot, E. V. (1991). Motivated strategies for learning questionnaire. Journal of Educational Psychology.
Saint, J., Fan, Y., Gašević, D., & Pardo, A. (2022). Temporally-focused analytics of self-regulated learning:vA systematic review of literature. Computers & Education: Artificial Intelligence, 3,100060.
Siadaty, M., Gasevic, D., & Hatala, M. (2016). Trace-based micro-analytic measurement of self-regulatedlvearning processes. Journal of Learning Analytics, 3(1), 183-214.
Winne, P. H. (2010). Bootstrapping learner's self-regulated learning. Psychological test and assessmentvmodeling, 52(4), 472.
Winne, P. H. (2020). Construct and consequential validity for learning analytics based on trace data.vComputers in Human Behavior, 112, 106457.