Personality Traits, Learning and Academic Achievements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>There has been an increased interest in personality traits (especially the five-factor model) in relation to education and learning over the last decade. Previous studies have shown a relation between personality traits and learning, and between personality traits and academic achievement. The latter is typically described in terms of Grade Point Average (GPA). This review paper gives an overview, based on previous research, of highly relevant factors that might explain the relation between personality traits and learning on the one hand and the relation between personality traits and academic achievement on the other hand. Motivation, goals and approaches to learning are important factors that are associated with some personality traits. Two conclusions can be made from this review: (1) intrinsic motivation, a deep approach to learning and learning goals are associated with general knowledge and good test results, all linked together by the openness trait; (2) extrinsic (in combination with intrinsic) motivation, an achieving (in combination with deep) approach to learning and performance goals (in combination with learning goals) are associated with high grades in general linked together by the conscientiousness trait. Openness is associated with learning and general knowledge while conscientiousness is associated with academic achievement.</p>
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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 | 0.000 |
| 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 | 0.001 |
| 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 it