The Journal of Applied Instructional Design
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the complexities of contemporary classrooms.Differentiating instruction to meet diverse student needs while navigating outdated and rigid curriculum guides can overwhelm educators.Addressing these pedagogical concerns, we conducted a mixed-methods study with 98 participants from the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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.098 | 0.029 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".