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- Vol 7 RE: Chapter 7, App D
self-regulated learningmetacognitive regulationhypermedia learninggoal settingcomprehension monitoringstrategy selectionadaptive scaffoldingJournal article Evidence summaryAt a glance
Azevedo reviews research on learning complex science topics with open-ended hypermedia and argues that such environments become useful metacognitive tools only when they support self-regulated learning.
Key ideas
Learners must coordinate prior knowledge, planning, monitoring, strategy use, motivation, and adaptation while navigating nonlinear information. Studies from the author's laboratory and classrooms show that learners of different ages often struggle to deploy these processes spontaneously. The article uses self-regulated learning as a framework for analysing both learning outcomes and the moment-by-moment processes that produce them. It also considers how prompts, human tutors, and adaptive computer scaffolds can model or support productive regulation.
Why it matters
The main implication is that access to rich digital resources does not by itself ensure deep learning: effective environments must help students set goals, activate relevant knowledge, monitor understanding, choose strategies, and revise their approach when comprehension breaks down.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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