Design for a Real-world Relevant “Introduction to Business”Course: Students Learning to be Players Instead of Spectators
Bibliographic record
Abstract
Business faculty, librarians and instructional design specialists at Memorial University of Newfoundland have partnered together to design a new Business 1000 course. The new course design addresses concerns from the corporate sector that business graduates lack real-world relevant knowledge and skills. This paper describes traditional “orthodox business school management education” which teaches students to look at contexts and situations, creating what we call management Spectators. We go on to describe a new model for management education which would put students into the here-and-now realities of business management and encourage situational analysis, creating what we call Players. The new model fosters critical thinking and information literacy skills in students to help them become more real-world relevant. In the new course design assignments and activities are structured as learning experiences rather than mere testing tools. In order to reflect the real-world environment students work in teams on business analysis and planning projects that may lead to a business plan. Through a “discovery learning” model, each team will necessarily learn about functional areas of business (accounting, finance, marketing, and organizational behaviour) in the course of completing the projects. Students will also hone their information literacy skills as part of the projects, learning how to identify information needs, where to get relevant and timely information, and how to critically analyze/evaluate the information they find.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".