A Trace of Motivational Theory in Education through Attribution Theory, Self-Worth Theories and Self-Determination Theory
Bibliographic record
Abstract
If motivation is the desire to act or move toward a particular activity, task or goal, just what influences one’s desire to do so remains complex. The impact of social context, or even just the perception of social context, can greatly influence what one attributes to their sense of self, as conveyed in attribution theory (AT), their perception of self-worth, as conveyed in self-worth theories (SWT) and subsequently their mindset and their behaviour to act, as conveyed in self-determination theory (SDT). Even more unclear is exactly what role the education system plays in fostering/hindering one’s motivation to learn. It is clear however, that the structure of the education system, the influence of educator’s actions and attitudes (whether deliberate or inadvertent), and the nature of peer competition can act as detrimental forces on the impact of one’s sense of ability and self. Educational policy that is created based on generalizations about universally innate human abilities, needs and drives, makes the question of how to foster intrinsically motivated students in schools even more challenging. Outside school programs such as Motivate Canada, which aim to foster motivation in youth by strengthening their self-confidence, and in-school programs, such as Inter-A, which aims to generate intrinsic, mastery orientated motivation, may not address all the complex factors underlying student motivation, but are a good start. Subsequently, motivational theories, despite their inconclusiveness provide hope that for students to grow into emotionally well-adjusted adults prepared to constructively contribute to our societies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".