Immersive Learning: Using a Web-Based Learning Tool in a PhD Course to Enhance the Learning Experience
Bibliographic record
Abstract
Aim/Purpose: Teaching and learning is no longer the same and the paradigm shift has not settled yet. Information technology (IT) and its worldwide use impacts student learning methods and associated pedagogical models. Background: In this study we frame immersive learning as a method that we believe can be designed by pedagogical models such as experiential, constructivist, and collaborative elements. We also present a peer-to-peer interactive web based learning tool, designed and implemented in-house with immersive learning features. Methodology: We conducted an exploratory research with a Ph.D course on “pedagogical methods” where 9 doctoral students were tasked to follow the peer-to-peer 3 phase process in their learning. Contribution: We found the peer-to-peer does favor experiential, constructivist, collaborative learning, which contributes into the use of immersive learning as an important learning style for the future. Findings: This study investigated different ways to measure students’ collaboration, constructivism through their peer evaluation scores and performance in an immersive learning environment by taking the roles of teacher, evaluator, and learner. Recommendations for Practitioners: An in-depth understanding of immersive learning methods allows the application of Experiential Immersive Learning (EIL) in various disciplines of professional training, which can increase performance and engagement. Recommendation for Researchers: It is necessary and advantageous for a researcher to view in-depth the process of students’ learning, to have the ability to quantify, analyze each individual’s contribution, and to observe via Information Technology the collaborative aspects of learning. Impact on Society: By observing an effective methodology in learning, this allows us to understand how knowledge is created throughout different disciplines. Future Research: Further studies should be made to adjust and polish our understanding of the peer-to-peer tool in order to gain a deeper understanding of customized learning.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".