Reflective Learning in Education Courses: Conceptualizations and Instructional Strategies
Bibliographic record
Abstract
Reflective learning is considered crucial for advancing learning strategies, improving professional practice, and engaging in lifelong learning. For this reason, developing reflective learning has been imperative for the education of professionals across disciplines. Despite its potential, however, there is a lack of conceptual clarity and models of good practice for developing students’ reflective skills. The purpose of this study is to review conceptualizations of reflective learning and related instructional strategies. We conducted a scoping review of 53 empirical studies obtained through a search of Education Source, ERIC, and PsycInfo databases. The results indicate a range of conceptualizations of reflective learning including content understanding, evaluation of perspectives, review of practice, metacognition, and examination of sociocultural contexts of education. Instructional strategies are discussed in terms of objects and process of reflection. We propose a conceptualization of reflective learning that goes beyond developing professional competencies to addressing broader issues relevant to everyday life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".