An Integration of Virtual Reality Into the Design of Authentic Assessments for STEM Learning
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
Creating equal learning opportunities for all students to develop STEM literacy and global competencies is an important educational initiative. Due to COVID-19 pandemic-related classroom disruptions, it is imperative to find new ways to engage and motivate students in STEM learning. This chapter looks at the potential integration of VR technology into the design and development of authentic STEM assessments to promote the STEM learning of elementary school students in both online and blended learning environments. The design principles of a VR-based authentic STEM assessment are guided by the following theoretical frameworks: VR technology, authentic assessment, criteria for authentic intellectual quality, patchwork text approach, and SOLO taxonomy. An example of the design of authentic STEM assessments is included. The chapter concludes with the authors' recommendations for building teachers' capacity in VR technology and authentic STEM assessments through preservice teacher preparation and inservice teacher professional development. Future research directions are also 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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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