Bibliographic record
Abstract
In order to make teaching activities more appealing, different techniques and strategies have been constantly employed. In this study, the strategy of “literature circles” was utilized to improve the text-analysis skills, reading desires, and interests of prospective teachers of Turkish. “Literature circles” was not chosen to be used as the sole strategy throughout the entire weekly class hours; instead, it was used only for one class hour of every weekly four-hour classes, being complementary to and supportive of other teaching activities. The study was carried out as action research. A total of 92 third-year students in two sections of the department of Turkish Education voluntarily participated in the study. In order to improve the students’ book reviewing skills and reading interests, “literature circles” was implemented for a period of 12 weeks for one class hour. At the end of the implementation of “literature circles” when the students’ reading comprehension pre-test and post-test scores were compared, a significant difference was observed. Based on the results, it may be concluded that “literature circles” is effective in developing students’ abilities to find the theme, main idea, and keywords in a text. Besides, the students pointed out that the implementation of this strategy increased their interest and desire for communication, their self-confidence, cooperative learning, critical thinking, reading objectively without bias, and independent reading skills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".