Self-Regulated Learning From Teaching to Self-Reflective Practice Ch6 Computing technologies as sites for developing self-regulated learning 指導教授: Chen,

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Presentation transcript:

Self-Regulated Learning From Teaching to Self-Reflective Practice Ch6 Computing technologies as sites for developing self-regulated learning 指導教授: Chen, Ming-puu 報 告 者: Chen, Wan-Yi 報告日期: Schunk, D. H., & Zimmerman, B. J. (1998). Self-regulated learning: From teaching to self- reflective practice. NY: The Guilford Press.

2 Promises about computing technologies and learning Little is known about instructional design issues that affect students’ learning with technology Self-regulation is inherent when learning is guided by goals of any sort Self-regulated learning are theorized to help students learn more of what they study, develop and sustain positive motivation, and practice and extend skills for leaning

3 Self-regulated learning Self-regulated learning is a dualistic construct with properties of an aptitude and an event 3+1 phase model characterizes self- regulated learning –Each phase transforms or constructs information, thereby creating the potential for metacognitive monitoring and control

4 Phase 1: perceiving the task –understandings and inferences about the domain of the task –Reflects memories about attributes such as interest and efficacy in this domain –Involves memories of tactics that might be enacted to address the task Phase 2: setting goals –This act of deciding among alternatives is the second phase of self-regulated learning –The product of this decision making creates a goal that the student has elected to pursue

5 Phase 3: enacting tactics –Two main parts: conditional knowledge (IFs) characterize a tactic’s appropriateness; cognitive operations (THENs) transform and construct information Phase 4: adapting tactics for self-regulated learning –Editing typically involves a process of mindful abstraction In each phase of self-regulated learning, metacogntive monitoring is central

6 Guiding phases 1 perceptions about effort and tasks A model of effort –For most learners in most situations, however, regulating most cognitive tactics requires effort Supporting learned industriousness within self- regulated learning –To hold a belief that challenging tasks should be approached with effort –Effortful tactics are applied, the experience involves more than just practicing a tactic –Learning system is to guide a student to use effort demanding tactics in learning activities –When tasks are challenging, those tasks are better approached with effort, and effort is realized by applying tactics that entail effort

7 Guiding phase 2 helping learners reshape goals Goal setting to guide selection of tactics –Students differentially self-regulation depending on the topic of goals, learning versus performance, bur this difference can be erased if monitoring is more frequent –Self-regulated learning are key variables that affect engagement

8 Guiding phase 3 supporting tactics that construct schemas Goal-free problems –Taught learners to perceive problem-solving activities as typically well served by a tactics called means-ends analysis –Two drawbacks Very demanding of working memory’s limited resources That it focuses the learner’s attention on step-step transitions

9 Guiding phase 4 adapting approaches to learning The testing effect, and the spacing effect suggest items students might edit in questioning tactics they use to study

10 Conclusions Students are goal-directed agents, they intrinsically n the midst of develop techniques for managing tactics that bring about learning Computing technologies also offer researchers powerful tools for studying self- regulated learning because adaptive systems can “work with” students in the midst of self-regulated instructional activities

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