Reinforcement of learning across the continuum of Education: A Scoping Review
Bibliographic record
Abstract
Introduction:Behaviorism is a paradigm of learning which covers various learning theories proposed by behavioral psychologists over the past century. Although, most of these theories have now become obsolete, due to a better understanding of the learning process, some terminologies such as Skinner’s “reinforcement” still find their place in modern day education. Reinforcement is a broad term and involves reiteration or enhancement of learning or behavior. Objectives: The present study undertakes a scoping review to identify evidence showing the role of reinforcement strategies on student learning in medical education Eligibility criteria: Research papers describing any teaching learning strategy or educational intervention that used Reinforcement of learning at any time during the learning process. Sources of evidence: Online databases were used to identify articles in the years 2009–2019, from which 10 publications from Canada & the United States and 6 from other nations were selected after meeting inclusion criteria. Charting methods: Data collected from the sources was charted with the help of a self-developed form, inclusive of Names of Authors & year of publication, type of article, country of origin, sample size, objectives & key findings of study Results: This scoping review shows that reinforcement strategies still have a high impact on student learning and reinforcing the taught material in medical education enhances student learning and retention. Conclusion: Reinforcement of taught material has a positive impact on student learning in modern day medical education.
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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 | 0.019 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".