Using Virtual Reality to Improve Learning Mindsets and Academic Performance in Post-Secondary Students
Bibliographic record
Abstract
Recent research indicates that most post-secondary students in North America "felt overwhelming anxiety" in the past few years, negatively affecting well-being and academic performance.Further research revealed that other emotions, biases, perceptions, and negative thoughts, not just anxiety, can similarly affect student academic performance.To address this problem, we classify these counterproductive thought processes, including anxiety, into a broader definition called Scarcity Mindset; a self-limiting perspective that appropriates cognitive bandwidth required for essential processes like learning in favour of addressing more critical needs or perceived insufficiencies.As such, through the lens of Scarcity Mindset, we conduct a multi-disciplinary literature analysis of innovative ideas in cognitive science, learning theories and mindsets, and current technology approaches that are suited to address the limitations of scarcity thinking.We identify strategies that help transition students to a more positive Abundance Mindset.We identify knowledge gaps, research questions, and a theoretical framework called the Cyclical Priming Methodology (CPM) that proposes priming interventions throughout the Experiential Learning Theory (ELT) cycle.These intervention strategies focus on student preparation, motivation, reflection, and the context of the learning environment.Our research will focus on determining whether these priming interventions, intended to induce abundance thinking, improves academic performance.We demonstrate that these priming intervention strategies with demonstrated empirical effectiveness within non-technology environments can transfer to leading-edge digital environments like Virtual Reality (VR). Further, building on the CPM and using a related technology-based cognitive therapy technique called Virtual Reality Exposure Therapy (VRET) as a comparable model, we propose a specific VR implementation of the CPM called VR Experience Priming (VREP).Four completed studies validate our hypotheses and key research questions: priming interventions performed within the context of our CPM and VREP theoretical model do improve academic performance and are transferable to VR to increase cognitive availability and academic performance in both preparatory and context priming conditions.Proving in our thesis for all priming conditions was beyond the scope of this research, but we plan to continue this research in 2022 and beyond.5.4.1.Overview .....
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".