The Journal of Applied Instructional 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
Too many middle years and high school students remain disengaged from history education, often perceiving it as irrelevant to their everyday lives and futures.While teachers aspire to design engaging history lessons, achieving this goal is challenging amidst the complexities of contemporary classrooms.Differentiating instruction to meet diverse student needs while navigating outdated and rigid curriculum guides can overwhelm educators.Addressing these pedagogical concerns, we conducted a mixed-methods study with 98 participants from the University of Saskatchewan to explore how virtual reality (VR) technologies can support the training and development of pre-service teachers in history curriculum and instruction.Guided by Allen & Sites' (2012) Successive Approximation Model (SAM), VR experiences were designed to immerse participants in two specific historical contexts: The Ottawa River Timber Slide and Agnes Deans-Cameron Magic Lantern Tour.Data collection methods included Likert scale surveys, open-ended questions, participant observations, and group debriefing sessions.The findings are synthesized within the context of the broader scholarly literature, bridging theoretical insights with practical applications.Based on the study results, we propose instructional design recommendations for integrating VR to support authentic, deep, and meaningful learning experiences in history education.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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