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Record W3168563371 · doi:10.21432/cjlt27853

Virtual Lab Integration in Undergraduate Courses: Insights from Course Design and Implementation

2021· article· en· W3168563371 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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVirtual labVirtual LaboratoryMathematics educationVirtual learning environmentEducational technologyTeaching methodCourse evaluationCourse (navigation)Instructional designComputer scienceMedical educationMultimediaPsychologyHigher educationEngineeringMedicine

Abstract

fetched live from OpenAlex

The instructors of four biology-related courses at a Canadian university integrated Labster virtual labs in their courses as a pre-lab activity, lecture substitute, or to provide lab experience in courses with no on-site labs. The instructors used a backward design approach to align the labs with the learning objectives of their courses and to connect the labs with their course assessments. A study was conducted to examine students’ perceptions of the usefulness of the virtual labs in terms of content knowledge and lab skills. At the end of each course, the instructors administered an anonymous survey in their classes. In total, 370 students participated. Across all four courses, survey results showed that at least 77% of the students found that virtual lab simulations helped them understand course concepts. At least 74% of the students navigated the virtual labs with no issues and 58% of the students found the simulations to be of high quality.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.392

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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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