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Record W1981021328 · doi:10.1109/t4e.2013.40

Learning from Blended Chemistry Laboratories

2013· article· en· W1981021328 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsAthabasca University
Fundersnot available
KeywordsFlexibility (engineering)Virtual labComputer scienceVirtual LaboratoryBlended learningIsolation (microbiology)Component (thermodynamics)MultimediaWork (physics)Distance educationEducational technologyMathematics educationEngineeringPsychology

Abstract

fetched live from OpenAlex

While laboratory or practical work is vital to many courses and programs within the sciences, it can also be the most challenging component to deliver effectively at a distance. In addition to the traditional face-to-face chemistry laboratory, Athabasca University has employed many alternative laboratories including virtual, remote and home-study laboratories to give our students the needed access and flexibility. Although the newer educational technologies often do provide viable substitutions to the traditional experience, it appears from initial results that combinations of modes offer even better returns on meaningful student engagement and learning. Therefore the future direction would be to further implement, study and explore alternative modes of laboratory delivery, not in isolation but by combining and blending them to optimize the student experience creating the teaching laboratory for the 21st century.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.997

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)0.0040.001

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 head0.003
GPT teacher head0.169
Teacher spread0.166 · 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

Quick stats

Citations5
Published2013
Admission routes1
Has abstractyes

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