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Record W4282842283 · doi:10.1021/acs.jchemed.1c01083

Remote Teaching of a Graduate-Level Instrument Repair and Maintenance Course Using Take-Home Kits and Laboratory Demonstrations

2022· article· en· W4282842283 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Chemical Education · 2022
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsTrent University
Fundersnot available
KeywordsMedical educationCourse (navigation)Graduate studentsCoronavirus disease 2019 (COVID-19)Computer sciencePsychologyEngineeringMedicine

Abstract

fetched live from OpenAlex

Because of the COVID-19 pandemic, most courses at universities in many countries transitioned to a remote format for the 2020–21 academic year. This presented additional challenges for courses taught with a hands-on component. Here we describe the implementation of a take-home activity kit and laboratory demonstrations to facilitate hands-on learning for a graduate-level instrument repair and maintenance course. Each student was provided with a take-home kit to enable hands-on activities at home, demonstrated by the course instructors during the synchronous lectures. Laboratory demonstrations were presented using short videos, photos, and instrument manufacturer instruction manuals. Student success was evaluated by means of a hands-on practical exam using the take-home kits and a student experience survey. All of the students who completed the survey indicated that they used the kit and felt that it improved their understanding of topics discussed in the synchronous lectures. The take-home kits and laboratory demonstrations enabled active remote learning that not only fulfilled the course learning objectives but also enhanced student experience and practical skills.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.266
Teacher spread0.245 · 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