Immersive Education in Practice: Pedagogical Considerations for Designing Deep and Meaningful Immersive Learning Experiences
Bibliographic record
Abstract
Modern teaching and training practices can be enhanced by engaging students in immersive experiences that are multi-sensory, interactive, and authentic. When augmented with social-emotional elements, these immersive experiences can facilitate deep meaningful learning (DML). Our panel will present three distinct case studies of DML in practice: 1) fostering historical thinking in undergraduate students through narrative immersion and game-based learning (GBL); 2) training adolescents' attention through virtual reality (VR) meditation and mindfulness activities; 3) building clinical reasoning capabilities in new graduate nurses through VR training scenarios. Each presenter will share practical insights and empirical evidence from their research on integrating immersive technologies and immersive storytelling to facilitate DML, highlighting the impact on learning outcomes and engagement. The case studies examine the role of VR and GBL in supporting reflective practice, critical thinking, and the development of interpersonal and intrapersonal skills among students and professionals. The panel aims to provide effective pedagogical strategies for designing immersive learning experiences, balancing the needs of diverse learners with technical capabilities, within authentic educational settings such as academic institutions and healthcare training environments.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".