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7 references
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- Vol 4 RE: App D
regulated learningtemporal processesadaptive regulationmultimodal dataprocess tracingmetacognitive monitoringphysiological measurestemporal alignmentJournal article Evidence summaryAt a glance
Järvelä and Bannert argue that regulated learning must be studied as a temporal and adaptive process rather than inferred only from retrospective questionnaires or final outcomes. Learners and groups continually interpret tasks, set goals, monitor cognition, motivation, emotion, and behaviour, and adjust strategies when conditions change.
Key ideas
Multimodal data—such as trace logs, discourse, video, eye movement, physiological measures, and self-reports—can reveal complementary aspects of these otherwise partly hidden processes. The promise lies not in collecting more streams indiscriminately but in aligning theory, timescale, units of analysis, and methods for detecting meaningful events and sequences. Data sources differ in what they measure and cannot automatically identify regulation; physiological synchrony or a click, for example, requires contextual interpretation. Researchers must integrate temporally aligned evidence, distinguish individual, co-regulated, and socially shared activity, and validate computational indicators against theoretically meaningful human judgements.
Why it matters
The article outlines methodological opportunities and cautions rather than offering a single predictive model. Its central agenda is adaptive support: process evidence may eventually help recognise when regulation is needed and provide well-timed scaffolds without oversimplifying learning.
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- Vol 2 TE: App B
goal orientation theorymastery goalsperformance goalsachievement motivationclassroom goal structuresself-regulated learningJournal article Evidence summaryAt a glance
Kaplan and Maehr review goal orientation theory, which explains achievement behaviour partly through the purposes learners perceive for engaging in a task. Mastery orientations emphasise understanding, improvement, and competence development; performance orientations concern demonstrating competence relative to others, with approach and avoidance distinctions adding complexity.
Key ideas
The authors argue that rapid expansion of measures and labels created theoretical vagueness, especially about whether orientations are stable traits, situational responses, or dynamic constructions. They outline six possible accounts in which goal orientations emerge from situation schemas, self-schemas, self-priming, psychological needs, values, or situated meaning-making. Convergent evidence links mastery-focused contexts with more adaptive engagement, while performance effects depend on definition, approach versus avoidance, and context. Classroom structures communicate what counts as success through tasks, authority, recognition, grouping, evaluation, and time.
Why it matters
The review cautions against treating questionnaire categories as fixed learner types or using simple prescriptions. Future research should clarify constructs, trace development and change, examine multiple goals and culture, and study how learners interpret situations while regulating attention, emotion, and action.
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- Vol 4 RE: Chapter 23
self-regulated learningteacher professional competencelearning strategy instructionmetacognitive reflectionprocess-focused feedbackteacher modellingJournal article Evidence summaryAt a glance
Karlen and Hertel introduce a special issue connecting teachers’ professional competence in self-regulated learning with the ways teachers promote it in ordinary classrooms. They position self-regulation as a future literacy that helps learners set goals, select and adapt strategies, monitor progress, manage motivation and emotion, and persist through changing demands.
Key ideas
Because these processes do not develop automatically, teachers need more than general encouragement to make students independent. The authors organize teacher support through the INSPIRE model: instructor, navigator, strategist, promoter, innovator, role model, and encourager. These roles include explicitly teaching and modelling strategies, designing environments that permit choice and reflection, diagnosing learner needs, giving process-focused feedback, demonstrating teachers’ own regulation, and sustaining students’ confidence and effort. The article also stresses teachers’ knowledge, beliefs, motivation, self-regulation, and diagnostic skills as conditions for effective practice.
Why it matters
It calls for closer connections among research, teacher education, professional development, and classroom observation so that self-regulated learning becomes an integrated feature of everyday instruction rather than an occasional add-on.
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- Vol 4 RE: Chapter 23
teacher self-regulated learningself-regulated learning promotionteacher competencestrategy knowledgediagnostic awarenessprofessional developmentJournal article Evidence summaryAt a glance
This study examines how teachers’ own self-regulated learning and their professional competence as promoters of self-regulation relate to students’ learning. The authors use a holistic competence framework that distinguishes teachers as self-regulated learners from teachers as agents who teach and support self-regulated learning.
Key ideas
Relevant competence includes knowledge of strategies and instruction, beliefs about learnability and value, motivation to promote self-regulation, teachers’ own strategy use, and classroom practices. Linking teacher and student data, the study tests whether these dimensions form pathways through which teachers influence students’ strategy knowledge, use, motivation, and achievement-related processes. The findings underline that teacher effects are not captured by a single disposition: different competence components make distinct contributions, and what teachers know or believe does not automatically translate into practice. Promotion is more likely when teachers possess coordinated knowledge, motivation, diagnostic awareness, and self-regulatory experience.
Why it matters
The article therefore argues for teacher education and professional development that integrate conceptual understanding with modelling, practice, observation, feedback, and reflection, enabling teachers to make self-regulated learning visible and teachable in everyday lessons.
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- Vol 4 RE: Chapter 24
self-regulated learningemotional intelligencestress copingpostgraduate wellbeingresearch-related pressureadaptive copingstudent supportJournal article Evidence summaryAt a glance
This quantitative study investigates how self-regulated learning, emotional intelligence, and coping with stress relate to postgraduate students’ wellbeing. The authors frame postgraduate research as a demanding activity that requires learners to organize time and resources, monitor progress, regulate motivation and emotion, and respond constructively to setbacks.
Key ideas
Survey data were used to estimate students’ levels on the focal constructs and examine correlations and predictive relationships with wellbeing. The reported analyses indicate positive associations between wellbeing and both self-regulated learning and emotional intelligence, while effective coping strategies also contribute to managing research-related pressure. The findings suggest that academic success and wellbeing should not be treated as separate concerns: planning, monitoring, emotional awareness, emotion management, and adaptive coping can jointly help students sustain productive research activity. The authors recommend that universities and supervisors provide explicit support for these capabilities through mentoring, counselling, workshops, and structured research guidance.
Why it matters
Because the study relies on self-report and a particular postgraduate context, its relationships should be interpreted as associative rather than definitive evidence of causation.
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- Vol 4 RE: Chapter 12
multimedia learningself-regulated learningcognitive loadgenerative activitiesmultimedia principlepersonalized explanationsstrategic monitoringcomputer-based instructionJournal article Evidence summaryAt a glance
Kuhlmann, Bernacki, and Greene connect cognitive theory of multimedia learning with the demands of self-regulated learning in computer-based higher education. Students must define tasks, plan, select strategies, monitor progress, and reflect, all of which consume limited mental resources.
Key ideas
The authors argue that well-designed multimedia can reduce unnecessary processing and leave more capacity for these regulatory activities. They illustrate how the multimedia principle can coordinate words and relevant graphics, personalization can make explanations more conversational, and generative activities can prompt learners to select, organize, and integrate content. These principles are presented as design supports, not replacements for students’ agency or explicit self-regulation instruction. The chapter is conceptual and practice-oriented rather than a comparative intervention study, so it does not establish that every multimedia feature improves regulation.
Why it matters
Its practical contribution is a useful design test: remove avoidable cognitive burden, align representations with the learning goal, and include activities that require meaningful processing so students can devote attention to both understanding content and managing their learning.
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- Vol 4 RE: App A, App C
- Vol 4 RE: Chapters 25, 27
artificial intelligenceself-regulated learningmetacognitive awarenessadaptive feedbacklearner agencyhuman-centred regulationlearning analyticsalgorithmic biasJournal article Evidence summaryAt a glance
Lan and Zhou qualitatively synthesise research on artificial-intelligence applications supporting self-regulated learning in higher education. They organise findings around phases and functions of regulation, including goal setting and planning, performance and monitoring, feedback and strategy adjustment, and reflection.
Key ideas
AI systems can collect and visualise learning data, recommend resources or pathways, provide adaptive prompts and feedback, support metacognitive awareness, and help learners make decisions. The review distinguishes human-centred regulation, in which learners retain access and control, from designs that risk outsourcing judgement to opaque automation. Evidence is uneven: studies concentrate on selected phases, tools, and short-term outcomes, while emotional and motivational regulation, long-term development, diverse populations, and transfer receive less attention. Challenges include privacy, bias, explainability, data quality, overreliance, learner agency, and whether recommendations genuinely improve regulation rather than compliance.
Why it matters
Because included interventions and methods are heterogeneous, the review does not establish a single effect size or prove that AI causes better learning. It calls for theory-grounded, longitudinal, and human-centred design in which AI augments learners’ awareness and choice and teachers remain responsible for pedagogical interpretation.
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- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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