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3 references
Panadero, Broadbent, Boud, and Lodge examine how formative assessment can help learners move from externally supported regulation toward more independent self-regulation.
Their model centers on evaluative judgement: the capacity to understand quality and make sound decisions about one’s own and others’ work. Teacher feedback, peer assessment, self-assessment, exemplars, and criteria can expose learners to assessment knowledge and regulatory strategies. Through these interactions, regulation is initially shared or co-regulated, then may be appropriated by the learner. The authors distinguish this developmental function from merely supplying corrective information and argue that students need repeated opportunities to judge quality and act on those judgements.
The paper offers a conceptual bridge between formative assessment and regulated-learning research, while identifying implications for designing assessment practices that develop durable learner agency.
Ren, Lee, and May systematically review how artificial intelligence has been used to support self-regulated learning in education.
Searching literature from 2004 through 2024, they retain twenty-seven studies and classify them by educational level, research method, subject area, AI technology, self-regulated-learning framework, and reported outcome. The review identifies intelligent tutoring and adaptive systems, learning analytics and prediction, conversational agents, and other data-driven supports that can prompt planning, monitoring, feedback use, strategy adjustment, and reflection. It also finds uneven theoretical grounding and limited evidence across contexts, with many studies concentrated in particular settings and phases of regulation.
The authors call for stronger alignment between AI functions and explicit self-regulation theory, more rigorous empirical designs, attention to learner agency and ethics, and research that tests sustained effects rather than short-term performance alone.
Ryan and Deci clarify intrinsic and extrinsic motivation through self-determination theory. Intrinsic motivation concerns doing an activity for its inherent interest or enjoyment.
Extrinsic motivation concerns instrumental reasons, but it is not uniformly controlling: regulation ranges from external pressure and internal compulsion through personally endorsed identification and integration. The quality of motivation therefore matters alongside its quantity. Environments that support the basic psychological needs for autonomy, competence, and relatedness foster intrinsic motivation, internalisation, persistence, learning, performance, and well-being; controlling rewards, threats, meaningless demands, or thwarted needs can produce more fragile engagement. Rewards do not have a single inevitable effect—their informational or controlling meaning, contingency, and context matter. The review connects definitions, theory, empirical findings, educational practice, cultural considerations, and research directions.
For educators, useful design combines optimal challenge, clear structure and feedback, meaningful rationales, acknowledgement of feelings, opportunities for agency, and caring relationships. Not every school activity will become intrinsically enjoyable, but learners can still understand its value and engage with a greater sense of ownership.
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