Developing and Implementing Lab Skills Seminars, a Student-Led Learning Approach in the Organic Chemistry Laboratory: Mentoring Current Students While Benefiting Facilitators
Bibliographic record
Abstract
This paper describes the successful adaptation of certain components of peer-led team learning (PLTL) as well as service learning principles into our initiative: lab skills seminars (LSS). These seminars were organized for large, second year organic chemistry laboratory courses. Prior to LSS, the only help available for students was traditional office hours conducted by the course coordinator. Such office hours are not sufficient or effective in large courses for numerous reasons, such that laboratory skill seminars were created to fill that gap and also diminish high levels of anxiety associated with complicated laboratories. Seminars were prepared and facilitated by upper year service learning students or volunteers, who had successfully completed the same course. Facilitators and course students mutually benefit significantly from these seminars, and this article will discuss these benefits in greater detail.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".