Investigation of Experiential Learning Practices in K-12 Education
Bibliographic record
Abstract
Experiential learning is a teaching strategy and theory that emphasizes the individual student's experience "... there is one permanent frame of reference: namely, the organic connection between education and personal experience” (Dewey, 1938, p. 25). What is known about experiential learning methodology conclusively is that students experience superior learning outcomes when experiential learning methods are used (Burch et al., 2019). What requires further investigation are the factors of assessing experiential education to determine what procedures contribute to the achievement of student outcomes. There is a lack of formal research in K-12 experiential learning and available experiential frameworks are insufficient when applied to K-12 education. Experiential resources focus primarily on the student’s progress through a set of defined procedures when supporting material for the facilitation of learning by educators are noticeably absent. This paper establishes the need for a base of instructors' understanding in K-12 experiential methods assessment. This research identifies areas for further exploration of concepts and future studies to develop relevant supports to facilitate meaningful experiential education in K-12 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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".