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Record W2760615841 · doi:10.1021/acs.jchemed.7b00073

Developing and Implementing Lab Skills Seminars, a Student-Led Learning Approach in the Organic Chemistry Laboratory: Mentoring Current Students While Benefiting Facilitators

2017· article· en· W2760615841 on OpenAlexafffund
Kalyani Sabanayagam, Vivek D. Dani, Matthew John, Wanda Restivo, Svetlana Mikhaylichenko, Shadi Dalili

Bibliographic record

VenueJournal of Chemical Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersUniversity of Toronto Scarborough
KeywordsMedical educationService-learningAdaptation (eye)PsychologyChemistryPedagogyMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.378
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2017
Admission routes2
Has abstractyes

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