Experiential learning and work placement impact for high school students: the need for high school cooperative placements
Bibliographic record
Abstract
This thesis explores the role of experiential learning in helping high school students understand their individual talents, interests, and purposes, with a particular focus on cooperative placements. The sections of this study investigate the direct effects these placements have on students' engagement and practical understanding of their chosen fields, highlighting the ways in which hands-on experiences contribute to more meaningful connections with their academic and career goals and discusses how these experiences facilitate a clearer understanding of individual abilities helping students with their career planning and individual decision-making. Furthermore, this study explores how real-world experiences shape students' choices in their high school courses and explores their readiness for post-secondary education and career pathways. An exploration of methodological approaches are presented throughout this investigation, addressing a different aspect of experiential learning and its impact on high school students. The thesis aims to contribute to a deeper understanding of how experiential learning can enhance educational outcomes by aligning students' academic experiences with their personal and professional aspirations, personalizing education for each student.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".