Mitigating Information Overload: An Experiential Exercise Using Role-Play to Illustrate and Differentiate Theories of Motivation
Bibliographic record
Abstract
Work motivation is a core component of many management courses. However, its effective teaching can be hampered by the fragmentation and seeming incoherence of the various theories of work motivation. To address this challenge, we describe an interactive role-play activity that induces students to synthesize, apply, and compare several theories of motivation. In the first part of the exercise, students work in small groups to prepare a role-play skit illustrating a specific theory of motivation. In the second part, groups present their role-play skits in front of the class, and the rest of the students try to determine which theories were performed. Next, the debriefing session encourages students to discuss, compare, and contrast the theories. Though the present exercise focuses on four theories—the hierarchy of needs, the two-factor theory, expectancy theory, and self-determination theory—the activity can be easily adapted to incorporate other models of motivation.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".