Association of Kinesthetic and Read-Write Learner with Deep Approach Learning and Academic Achievement
Bibliographic record
Abstract
Background: The main purpose of the present study was to further investigate study processes, learning styles, and academic achievement in medical students.Methods: A total of 214 (mean age 22.5 years) first and second year students - preclinical years - at the Asian Institute of Medical Science and Technology (AIMST) University School of Medicine, in Malaysia participated. There were 119 women (55.6%) and 95 men (44.4%). Biggs questionnaire for determining learning approaches and the VARK questionnaire for determining learning styles were used. These were compared to the student’s performance in the assessment examinations.Results: The major findings were 1) the majority of students prefer to study alone, 2) most students employ a superficial study approach, and 3) students with high kinesthetic and read-write scores performed better on examinations and approached the subject by deep approach method compared to students with low scores. Furthermore, there was a correlation between superficial approach scores and visual learner’s scores.Discussion: Read-write and kinesthetic learners who adopt a deep approach learning strategy perform better academically than do the auditory, visual learners that employ superficial study strategies. Perhaps visual and auditory learners can be encouraged to adopt kinesthetic and read-write styles to enhance their performance in the exams.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".