Comparing Objective Measures and Perceptions of Cognitive Learning in an ERP Simulation Game
Bibliographic record
Abstract
Enterprise Resource Planning (ERP) systems have had a significant impact on business organizations. These large systems offer opportunities for companies regarding the integration and functionality of information technology systems; in effect, companies can realize a competitive advantage that is necessary in today’s global companies. However, effective training for the incorporation and use of these large-scale systems is difficult and challenging; improved strategies for effective training include the use of business simulations. The question of the effectiveness of training remains—“How do we measure learning?”. In a recent Simulation & Gaming article “Business Simulations and Cognitive Learning”, Anderson and Lawton (2009) focus on research associated with the assessment of cognitive learning in business simulations. They indicate that little progress has occurred in objectively assessing cognitive learning in simulations and call for research that might help determine whether simulations accomplish what they purport to achieve in terms of participant learning. In this research note, objective measures of learning are presented. The results of objective measures of learning are compared with those of self-assessed perceptions of learning in the context of an ERP business simulation game. Based on the comparisons of learning measures, self-assessed measure results were not different from those of objective measures; moreover, learning did occur.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".