Bibliographic record
Abstract
Virtual environments are inherently social spaces where user productivity and collaborative learning can take place. However, the majority of existing studies to date investigate common behaviours such as multi-tasking within traditional face-to-face learning environments. As part of a thesis dissertation, this study investigated the importance of structuring learning environments to maximize learning and minimize virtual distractions. Using an OpenSim virtual environment, the researchers conducted an experimental study during the Fall 2013 and Winter 2014 terms with 91 undergraduate students at the University of Alberta. The study investigated the influence of participants’ prior computer experience, cognitive learning styles and extroversion-introversion on the impact of passive and social distractor tasks during learning and recall of factual information in virtual environments. The results indicated that prior video game use is a significant predictor of lower overall test time and higher overall test score, but the software recognition test, social networking use and virtual world use did not have a significant impact on learning performance. While extroverted individuals tended to complete questions faster under the interactive-type distractor condition, they achieved higher accuracy scores under the passive or no distractor-type conditions. Introverted individuals tended to complete questions faster and more accurately under the no distractor-type condition. In addition, the study found that field independent participants outperformed field-dependent counterparts by an average test score of 0.86 at approximately the same speed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".