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Record W3177188188 · doi:10.1177/03064190211025954

A build-at-home student laboratory experiment in mechanical vibrations

2021· article· en· W3177188188 on OpenAlexafffund
G. S. Schajer

Bibliographic record

VenueInternational Journal of Mechanical Engineering Education · 2021
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSet (abstract data type)Coronavirus disease 2019 (COVID-19)Bifilar coilComputer scienceSimple (philosophy)MultimediaHuman–computer interactionEngineeringMathematics educationPsychologyMedicine

Abstract

fetched live from OpenAlex

The Covid-19 pandemic has caused many university educators to redesign their teaching to online delivery. This can be an effective approach for theoretical and conceptual teaching, but it is challenging to provide practical laboratory experiences. The objective here is to design a hands-on laboratory experience that can safely be undertaken by students remotely and that has substantial educational content. A new experiment was designed featuring a bifilar pendulum that students build themselves from readily available low-cost materials. This simple vibrating system has a surprisingly rich set of interesting physical characteristics that provide several important learning points. Initial trials indicate good student experience with the new experiment, notably an appreciation for the "do-it-yourself" aspect of the apparatus construction. The self-directed features and multiple learning features of the new student experiment make it attractive for use during Covid-19 times and beyond.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.002

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 head0.006
GPT teacher head0.275
Teacher spread0.269 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations13
Published2021
Admission routes2
Has abstractyes

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