Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".