MétaCan
Menu
Back to cohort
Record W3120615026 · doi:10.22329/jtl.v14i1.6234

Teaching Biochemistry and Molecular Biology with Virtual Reality — Lesson Creation and Student Response

2020· article· en· W3120615026 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Teaching and Learning · 2020
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsVirtual realityVirtual LaboratoryComputer scienceSoftwareCommercializationMultimediaHuman–computer interactionComputer graphics (images)

Abstract

fetched live from OpenAlex

The commercialization of Virtual Reality (VR) hardware has enabled the use of VR as an educational tool. We describe how a VR platform was used to create molecular visualizations using standard PDB files with the purpose of delivering biochemistry and cellular biology lessons for undergraduates. Specifically, we describe two new software modules, one for rendering molecules generated from PDB files and one for creating interactive lectures. These modules were used to create two VR labs, one on DNA and collagen and the other on hemoglobin. We then describe how these labs were delivered, along with surveys and quizzes, to over 100 students in undergraduate biology courses. Student response to the labs are found to be extremely positive and the labs themselves are found to satisfy their educational objectives.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.018
GPT teacher head0.326
Teacher spread0.308 · 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