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Record W4403794050 · doi:10.24908/pceea.2023.17011

Evaluation of Student Perceived Learning Effectiveness and Motivation for a Capstone Design Web-based Virtual Reality Activity

2024· article· en· W4403794050 on OpenAlexafffundvenue
Kimia Moozeh, Michael Chabot, Saad Chahine, Paul Hungler

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsQueen's University
FundersQueen's University
KeywordsCapstoneVirtual realityComputer sciencePsychologyHuman–computer interactionMultimedia

Abstract

fetched live from OpenAlex

This study reports on the second implementation of a chemical processing plant web-based VR for a fourth-year chemical engineering capstone design course. The web-based VR provides a high fidelity representation of an ampicillin processing facility. To examine the effect of the web-based VR on student motivation and perceived learning effectiveness, a broken plant scenario was developed, and the class was divided into two groups: paper based, and web-based VR. Students completed the scenario in teams, either as a paper-based activity or using the web-based VR. This was followed by completion of an individual tutorial assignment and a survey. Survey findings indicated that students in the web-based VR group had a significantly higher positive response for motivation and perceived learning effectiveness compared to students in the paper-based group. However, students in the paper group scored higher on the individual follow-up assignment. Possible factors contributing to the results are discussed.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score1.000
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.032
GPT teacher head0.329
Teacher spread0.297 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2024
Admission routes3
Has abstractyes

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