"The Da Vinci Code for IP Research": Case Study of a Course-Integrated Educational Escape Room for Entrepreneurship Education
Bibliographic record
Abstract
Educational escape rooms can engage students in the development of information literacy skills while interacting with information formats and environments authentic to their disciplines. In business and entrepreneurship education, escape rooms offer the additional benefit of developing the characteristics of the entrepreneurial mindset, such as adaptive thinking, problem solving, enthusiasm, and decisiveness. This case study explores game-based learning for library instruction in entrepreneurship education. Using Eukel and Morrell’s (2021) escape room design cycle as a framework, it analyzes the development, testing, implementation, and results of an original escape room, Exfiltration! A Competitive Intelligence Virtual Escape Room, implemented as a course-integrated information literacy learning activity in an upper-level undergraduate new venture creation course. Limitations of the escape room are identified, and opportunities for iterative improvement are described. Recent scholarship on the application of entrepreneurial mindset, business research competencies, gamification, escape room pedagogy, and escape rooms in library instruction is discussed. This case study responds to the call from Taraldsen et al. (2020) for more small-scale studies of educational escape rooms outside of the STEM and health science disciplines.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".