Experiential Learning Interventions and Healthy Eating Outcomes in Children: A Systematic Literature Review
Bibliographic record
Abstract
Experiential learning is the process where learners create meaning from direct experience. This systematic review aimed to examine the effects of experiential learning activities on dietary outcomes (knowledge, attitudes, behaviors) in children. Four databases: Education Research Complete, Scopus, Web of Science and PsychINFO were searched from database inception to 2020. Eligible studies included children 0-12 years, assessed effect of experiential learning on outcomes of interest compared to non-experiential learning and were open to any setting. The quality of studies was assessed using the revised Cochrane risk of bias tool by two independent reviewers and effect size was calculated on each outcome. Nineteen studies were conducted in primary school, six in pre-school and one in an outside-of-school setting and used nine types of experiential learning strategies. Cooking, taste-testing, games, role-playing, and gardening were effective in improving nutrition outcomes in primary school children. Sensory evaluation, games, creative arts, and storybooks were effective for preschool children. Multiple strategies involving parents, and short/intense strategies are useful for intervention success. Experiential learning is a useful strategy to improve children's knowledge, attitudes, and behaviors towards healthy eating. Fewer studies in pre-school and outside of school settings and high risk of bias may limit the generalizability and strength of the findings.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".