Exploration of Technology-aided Education: Virtual Reality Processing Plant for Chemical Engineering Process Design
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.
Bibliographic record
Abstract
This work-in-progress study will explore technology aided education in the form of a Virtual Reality (VR) application used to support learning outcomes in a chemical engineering capstone course. VR has the ability to immerse users in a simulated environment and provide them with experiential learning opportunities. Most undergraduate chemical engineering students are required to design a chemical plant for their capstone design project without ever having visited or interacted with a full-scale processing plant and could benefit from the immersive experience that the VR tool would offer. This study will be conducted over a two-year period from September 2019 to May 2021. During the first-year, surveys and design challenges will be conducted without the use of the VR chemical processing plant. The data from the first year will establish a baseline that evaluates how learning outcomes are being met by the course without the VR application. During the second year the surveys will be given again in conjunction with the VR educational tool. The tool will give students the ability to view and interact with the unit operations inside a chemical processing plant without special training, expensive protective equipment and security clearance. Students will complete a number of challenges in VR and will be evaluated on their comprehension and invited to provide feedback on the effectiveness of the VR tool. The effects of VR on student comprehension, retention, and chemical processing design competency will be evaluated based on the data collected. This paper will discuss the initial design of the VR chemical processing plant, data from the non-VR cohort and a description of the research methods to be used during the final portion of the research.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it