Improving writing abilities by using GLIL in teaching Geography
Bibliographic record
Abstract
Research work answers the questions like what is the effectiveness of the CLIL method in the development of speaking and writing skills of students in geography lessons? The main problem in the study is the emergence of difficulties in understanding the content of the subject in students studying geography in a second language. The reason is the uncertainty of the content of geography in the Kazakh language by the students of the Russian class, as a result of which the idea cannot be written in the Kazakh language during the performance of the forming and summary assessment tasks. The relevance of the study lies in the application in practice of integrated subject and language learning in modern samples of lessons. Integrated learning subject matter and language /GLIL/ learning the subjects through a second/third languages. The study provided for the simultaneous implementation of subject and language objectives for educational purposes. The study used methods of questioning, interview, communication, communication with a psychologist, curator, parents, teachers of the Kazakh language. During the lesson, various approaches of the GLIL method aimed at the formation of subject knowledge were studied, as well as the positive and negative sides were evaluated. The study provides examples of writing by GordanStorbort, Alan Crawford, and studied the second language and studied the subject area, the mastery of language is the main achievement of the student in the integration of the subject and language, as well as a method aimed at the study of the subject and the study of language through the subject. Jim Cummins in 2004, as a result of his study by immigrants in Canada, determined that it takes two or three years to learn a second/third language to acquire basic interpersonal communication skills, and it takes five years for cognitive academic skills, and we are confident that the results of the research will be clear in the coming years.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".