More Real-world Relevant Learning for Business Students: An Online, Self-learning Tutorial for Finding and Critically Analyzing Information In and Of the Situation at the Time
Bibliographic record
Abstract
Concerns that business school graduates, program curricula and classroom teaching are lacking in real-world relevance have been on-going for almost as long as schools have been established. Traditional, theory-based and classroom-oriented teaching has not been leading to relevant learning. Students need to develop real-world situational relevance through learning to use their knowledge as the basis for developing their critical thinking, to search for and find information in and of the situation at the time, and to follow this with problem analysis and action planning. Strategic Management courses require students to complete business case situation analyses and make evidence-based recommendations for future strategy. Students have difficulty in doing this because they do not have the critical thinking and situation analytical process and procedures that they need to use when no-one is around to tell them what to do! Within an overall situational strategic management approach an on-line tutorial was developed to support web-based distance courses in Strategic Management. This was designed to enable students experientially to learn critical and analytical thinking processes in finding data, creating information and making findings and conclusions. The tutorial is based on a business case exercise wherein students are required to undertake a market analysis. It takes students step-by-step through the in-situational search for data, structured analysis, and interpretation of the information to give findings and conclusions. Skills gained from using the Situational Strategic Management Approach (SSMA) to analysis also are outlined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".