A survey of business educational simulations and their adoption by business educators.
Bibliographic record
Abstract
A survey was designed to investigate the current usage of business simulations in academic education. The purpose of the study was to discover (i) differences between current users, non-users and former users of educational simulations; (ii) modes of communication used for information; (iii) reasons for adoption and; (iv) currently used simulations. An Internet survey invited 14,497 educators from the professional organizations of ABSEL, ISAGA and AACSB member affiliated business schools. The invitations were accepted by 1085 respondents who were categorized into (i) 30.5% current business simulation users; (ii) 17.3% non-users; and (iii) 52.2% former users. It was found that users and former users have no significant differences in demographic and attitudinal characteristics between them. However, non-users have differences in attitudes that distinguish them from users. Lastly, the communication channels and the currently used simulation game titles were analysed. (Abstract shortened by UMI.) Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .G49. Source: Masters Abstracts International, Volume: 43-01, page: 0025. Adviser: W. Wellington. Thesis (M.B.A.)--University of Windsor (Canada), 2004.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".