Comparison of “Course Study and Learning Strategies” Used by 9th and 12th Grade Students in Anatolian and Science High Schools
Bibliographic record
Abstract
The main aim of this study is to comparatively examine the “Studying and Learning Strategies” used by the students of Science and Anatolian High Schools, which are the general secondary instruction level in the Turkish Educational System, at Adana province example in Turkey. The study is a descriptive study, a relational survey model. The population of the study is 9th and 12th grade students who are studying in Anatolian and Science High Schools in the city center of Adana. This study group of this research is consisted of 393 students in 9th and 12th grades who were studying at Adana Science High School and Seyhan İMKB Anatolian High School in the spring term of the 2019-2020 academic year. As a data collection tool, the “Study and Learning Strategies Scale for High School Students” developed by Kutlu, Yapıcı and Korkmaz (2015), consisting of 46 items and five sub-dimensions, was used. As a result of the study, when the “Study and Learning Strategies” of the students are examined according to school types; It has been determined that there is a significant difference in the sub-dimensions of “Repetition” and “Enjoying Learning”. It has been observed that there is a significant difference between male and female students in “Repetition” and “Self-Confidence” sub-dimensions according to the gender of the students. There is no significant difference between the levels of 9th and 12th grade students studying at Science and Anatolian High Schools according to their grade levels.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".