An Exploratory Study on the Impact of Collective Immersion on Learning and Learning Experience
Bibliographic record
Abstract
This paper aims to explore the impact of a collective immersion on learners’ engagement and performance. Building on Bandura’s social learning theory and the theory on the sense of presence, we hypothesise that collective immersion has a positive impact on performance as well as cognitive, emotional and behavioural engagement. Ninety-three participants distributed in four conditions took part in the experiment. The four conditions manipulated the collective and individual dimensions of the learning environment as well as the high and low immersion of the learning material. The two conditions that offered a high immersion setting used two types of the novel immersive dome: a large one for collective immersion and a small one for individual use. All participants were presented with the same stimuli, an 8-min-long video of a virtual neighbourhood visit in Paris in the 18th century. The participants’ reactions were measured during and after the task. The learning outcome, as well as the cognitive, emotional and behavioural engagement, were measured. Final results showed that collective immersion learning outcomes are not significantly different, but we find that collective immersion impacts the cognitive, emotional and behavioural engagement of learners.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".