1. Self-regulated learning in virtual communities Presenter: Zong-Lin TsaiAdvisor: Ming-Puu Chen Date: July 1, 2009 Delfino, M., Dettori, G. & Persico, D. (2008). Self-regulated learning in virtual communities. Technology, Pedagogy and Education, 17(3), 195-205.

2. Introduction • Boekaerts, Pintrich, and Zeidner (2000) argue that a lack of social learning experiences is the first important source of self-regulatory dysfunctions. • Virtual Learning Communities (VLCs), and in general Computer Supported Collaborative Learning (CSCL), appear to be a good way to reconcile individualisation and collaboration.

3. Introduction • Having room for individual movement, however, does not necessarily mean being able to make good use of it. In order to take advantage of their liberty, learners need to be able to suitably manage their own learning process. • Make plans on how to proceed; • Enacting those plans; • Monitoring their fulfillment; • Checking the quality of the work done.

4. Introduction • Carrying out such activities entails the adaptive reuse of knowledge and strategies previously learned in other contexts as well as the ability to evaluate and improve one’s own outcomes, without an explicit guide from some external mentor. In other words, learners must be able to self-regulate their activity, at least to some extent.

5. Method • The course lasted 12 weeks and involved 95 students and seven tutors who exchanged, in total, 7605 messages. • Activity 1 consisted of a role-play scenario, where students were requested to take on the role of strongly characterised teachers and to discuss strengths and weaknesses of a WebQuest from such different points of view. • Activity 2 was a case study on school-based learning communities. Trainees were asked to discuss assets and flaws in a school project recently carried out by a small group of teachers with their classes, based on the documentation provided.

6. Method • Preliminary hypotheses: • The trainees increased their self-regulation while progressing throughout the course, from Activity 1 to Activity 2; • Socialindicators were more frequent than individual indicators, regardless of the activities; • Cognitive/metacognitiveindicators were more frequent than emotional/motivational ones; • Activity 1 entailed less planning than Activity 2 because of the nature of the task assigned.

7. Method

8. Findings

9. Findings • The hypothesis formulated based on those data was that students were learningto participate and to self-regulate. The new data lead us to discard that hypothesis. • Based on this reflection, a well-structured, constructivist online course appears to be agood opportunity to practice SRL in collaboration with peers but probably does not significantlyincrease the ability to self-regulate, at least over a short period of time.

10. Findings • One reason is that VLCs tend to favor the social aspectsof SRL more than its individual aspects, especially when the assigned task is groupwork. • The second explanation is that, whenwriting messages in online collaborative environments, students are more likely to deal withmatters concerning the group rather than themselves as individuals.

11. Findings • The considered activities were content-based ones, in which students were assigned specifictasks, entailing collaboration on content-related themes, concepts and ideas.

12. Findings • We note that the evaluation clues have more or less the same frequency in the twoactivities, while planning is more frequent in Activity 2 and monitoring in Activity1. • We can therefore repeat here the same reflections made concerning the total number of SRL indicators: there does not seem to be a direct influence of the task assigned on the different phases of SRL.