Situated Motivation: An Empirical Test in an Accounting Course
Bibliographic record
Abstract
Abstract The enhanced motivation and performance benefits associated with the use of enriched work environments (i.e., high task identity, variety, and significance; worker autonomy; and frequent performance feedback) have been well established (Hackman & Oldham, 1975, 1976, 1980). The present study tests whether these benefits can also be achieved in the classroom setting. Students from a compulsory final‐year accounting course were asked to report their level of motivation in and their perceptions about the enrichment of the course. As hypothesized, a positive and significant correlation was found between student perceptions of enrichment and their reported motivation. The results, obtained by running a series of regression equations which included a variety of teaching context and student level variables, are also reported. These regression results offer further insight into the relationship between enriched learning environments and motivation. Résumé Une motivation rehaussée et une augmentation des performances sont associées à la mise en place d'un environnement de travail enrichi (par exemple grâce à une «high task identity» oú l'ouvrier reçoit une explication globale de son processus de travail et s'épanouit dans cette connaissance, la variété et l'importance de ce travail, l'autonomie de l'ouvrier et de fréquentes appréciations sur ses performances), comme cela a été clairement établi (Hackman & Oldham, 1975, 1976, 1980). Cette étude a pour but d'examiner si ces améliorations peuvent être étendues à la salle de classe. Des étudiants de dernière année dans un cours obligatoire de comptabilité ont été sollicités pour rendre compte de leur niveau de motivation pour le cours et de la proportion de tâches enrichissantes qu'ils avaient trouvée dans celui‐ci. Conformément à l'hypothèse de départ, une forte corrélation a été trouvée entre la perception des étudiants quant au nombre de tâches enrichissantes dans le cours et leur niveau de motivation. Les résultats obtenus à partir d'une série d'équations régressives, qui incluaient une diversité de contextes d'enseignement et des étudiants de niveaux variables sont également inclus. Ces résultats approfondissent encore la relation entre un environnement d'apprentissage enrichi et la motivation qui en découle.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".