The purpose, adaptability, confidence, and engrossment model: A novel approach for supporting professional trainees’ motivation, engagement, and academic achievement
Bibliographic record
Abstract
Trainees often decide to pursue a career in the professions because they see it as a means to attain their life goals: to become the kind of person they want to be, to live the kind of life they want to lead, and to make the kind of impact they want to have on the world. The life goals trainees pursue through a professional career are derived from their conception of their ideal self and are thus the most important and self-defining goals that they possess. In this article, we propose a novel approach for designing training activities to harness the motivational potential of professional trainees’ life goals, toward supporting their deep engagement in training activities. We propose that activities can be designed to help trainees make links between (1) the concepts and skills covered in an activity, (2) the professional practice behaviors that an activity prepares them to enact, and (3) the life goals that are served by enacting these professional practice behaviors. Informed by Control Theory and Self-Determination Theory, we predict that this design may promote trainees’ adoption of activity-specific goals that emphasize deep understanding, long-term retention, and transfer, and enhance their autonomous motivation to attain their goals. However, there are some situations when this design may be less effective for, or even harmful to, trainees’ motivation. Specifically, we predict that helping trainees establish a purpose for learning by linking an activity to their life goals may be most effective when they can adapt an activity to best align with their purpose, when they are confident in their ability to attain their activity-specific goal, and when they become engrossed in an activity because it affords interest- and curiosity-stimulating actions. We package our theoretical analysis into the PACE model of motivational design. When our predictions are supported by more empirical evidence, the model can help educators set the PACE for trainees, thereby motivating them to engage deeply in training activities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".