Motivating the Academically Unmotivated: A Critical Issue for the 21st Century
Bibliographic record
Abstract
Interests and goals have been identified as two important motivational variables that impact individuals' academic performances, yet little is known about how best to utilize these variables to enhance childrens' learning. We first review recent developments in the two areas and then examine the connection between interests and goals. We argue that the polarization of situational and individual interest, extrinsic and intrinsic motivation, and performance and mastery goals must be reconsidered. In addition, although we acknowledge the positive effects of individual interest, intrinsic motivation, and the adoption of mastery goals, we urge educators and researchers to recognize the potential additional benefits of externally triggered situational interest, extrinsic motivation, and performance goals. Only by dealing with the multidimensional nature of motivational forces will we be able to help our academically unmotivated children.
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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 | 0.028 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.018 |
| 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".