Grammar Teaching in the Turkish Language Course Curriculum: An Examination in the Context of Acquisitions, Activity, and Teachers’ Opinions
Bibliographic record
Abstract
This research aimed to reveal teachers’ opinions about how the grammar-teaching process is related to the acquisitions in the curriculum, activities, and acquisitions presented in the textbooks teaching in mother tongue, the study reviewed different textbooks from four publishers, Cem, Sonuç, Koza, and Ministry of National Education (MEB) publications prepared for the 2018 primary school Turkish language course curriculum and Education Information Network (EBA) of the Turkish Ministry of Education. Moreover, opinions of ten primary school teachers who have taught all grades regarding the teaching process for grammar acquisition were investigated. The study conducted a case study method, which is one of the qualitative research techniques. Besides, a document analysis was conducted to obtain the research findings. Structured interview protocol and document review were used as the data collection tool. The findings of the study revealed that acquisition in the learning areas of reading and writing for grammar in the curriculum was not clearly and transparently identified, and the limits of the teaching framework were not specified. The study findings also revealed that information was transferred only in the majority of the grammar activities in the textbooks. Also, classroom teachers reported that they used different methods on the subjects they could embody in the teaching process, but they claimed to have difficulties in teaching abstract concepts.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".