ENHANCING MOTIVATION AND ENGAGEMENT IN ECONOMICS COURSES FOR ‘GENERATION M’ STUDENTS
Bibliographic record
Abstract
As faculties, we all continuously try to improve our teaching through various mechanisms -adoption of new ideas, processes and procedures. However, we often find a gap between our understanding of what students learn and what they learn. The entire premise of today's learning is based on a teacher-student hierarchical model. The models of enhancing student motivation suggest breaking out of this hierarchical transfer of knowledge. Despite the breadth and quality of existing SoTL work, surprisingly little is known about how students themselves characterize their learning experiences. The few studies that have prominently carried the "voices" of university students date back to the 1980s and therefore do not incorporate the insights of an entirely new generation -the Millennials or generation M. All the other priorities of the general life and academic life of generation M compete with their motivation to learn. This paper fills a gap in research by analyzing the opinions of generation M students, attempting to understand what factors are related to the motivation, engagement and participation of generation M undergraduate students in economics courses, and examining how students' motivation may contribute to their success and failure in economics courses, as well as what can be done to increase their motivation.
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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 | 0.001 |
| 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 | 0.000 |
| 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".