Learning Styles, Personality Types and Reading Comprehension Performance
Bibliographic record
Abstract
This study aims at reviewing the relationship between learning styles, personality and reading comprehension performance. In the last two decades, ample studies have been done to examine the relationship between learning styles, learner’s personality and performance in academic settings. The reviewed studies substantiate that there is a relationship between personality types and/or traits of the learners, the way they establish their learning styles and their academic success in school and university both at an undergraduate and postgraduate level. Therefore, learners depending on the type of their personality resort to different learning styles or preferences which-in turn- affect their learning performance. However, there are no studies – either theoretical or empirical – examining exclusively the role of personality and learning styles on reading comprehension performance. Moreover, the findings with regard to the bulk of research on the relationship between personality and success in reading comprehension- are not that congruent. Accordingly - due to the scarcity of the research on showing the relationship between personality, learning styles and achievement in reading comprehension, and also incongruity of the research results on personality and reading comprehension performance - the current study proposes that further research on the above areas would be of the great need.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".