An Investigation of the Relationship Between University Prep Class Students’ Intelligence Types and Their Success of Foreign Language Learning
Bibliographic record
Abstract
In the 21st century, when foreign language learning has become almost compulsory, everyone is trying to fulfill thisobligation. In fact, foreign language has become a very important thing not only for the people who work onlinguistic sciences but also for politicians, academicians, and even sportsmen. In this context, the main purpose is tocompare the English achievement levels of students with physical/kinesthetic intelligence to those with otherintelligence areas. 198 males, 66 females, in total 267 students, who study at Karabuk University, School of ForeignLanguages during the summer term of 2017-2018, have participated in this study. “Personal Information Form”developed by researchers; “Multiple Intelligence Observation Form” from the book “Multiple IntelligenceApplications” (2003) by Selçuk, Kayılı, Okut; Karabük University Preparatory Summer School foreign languagesuccess averages for determining the English achievement levels of the students, have been used as data collectiontools. Kolmogorov Smirnov, Mann-Whitney U, Kruskal Wallis, and Spearman Correlation tests have been used toanalyze the data. Consequently, there are no significant differences between the foreign language scores of thestudents according to the variables of doing sports with a license, gender, age, and licensed sports branches. However,there is a significant difference between the foreign language scores of the students according to the variable ofexercising regularly (p=0.04<0.05). There is no statistically significant relationship between students' multipleintelligences and foreign language scores.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".