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Record W2959271817 · doi:10.1021/acs.jchemed.8b00815

Implementation of a Student-Customized Integrated Upper-Level Chemistry Laboratory Course

2019· article· en· W2959271817 on OpenAlexafffund
Vishakha Monga, Kerry J. Knox, Elizabeth A. L. Gillis, Robin Stoodley, Guillaume Bussière, Christine Rogers

Bibliographic record

VenueJournal of Chemical Education · 2019
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsAffordanceCurriculumWork (physics)Course (navigation)Mathematics educationStrengths and weaknessesCourse evaluationComputer scienceEngineering ethicsChemistryHigher educationEngineeringPedagogyPsychologyHuman–computer interaction

Abstract

fetched live from OpenAlex

The implementation of an integrated approach to upper-level undergraduate chemistry laboratory instruction that incorporates student choice both in the selection and sequencing of the experimental work is presented. The approach involves combining laboratory work in several traditional subdisciplinary areas of chemistry in a single course. Experimental work in emerging areas of chemistry is also incorporated, as are experiences intended to capture an interdisciplinary approach. Logistical affordances, the nature of the resulting learning environment, student responses, and faculty experiences are explored through analysis of curriculum documents and the use of student surveys. The work reveals several strengths and weaknesses in terms of logistics and learning environment, insights into how students engaged with the course and the aspect of choice, and associated benefits and challenges for faculty members. Implications for practice are presented with the aim of informing educators and institutions considering or adopting a similar approach.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.003

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 head0.014
GPT teacher head0.361
Teacher spread0.347 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations10
Published2019
Admission routes2
Has abstractyes

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