Kazakhstani In-Service Schoolteachers’ Perceptions of Differentiated Learning in Heterogeneous EFL Classrooms
Bibliographic record
Abstract
In the era of the fourth industrial revolution, it is necessary to create a new model of a teacher and learner to provide global competitiveness. This study aims to explore Kazakhstani in-service English as a foreign language teachers’ perception of awareness, implementation, and managing differentiated learning, lesson planning, and designing in heterogeneous English as a second language classrooms. The present study was done during the academic year 2020-2021 among the in-service English as a foreign language teacher from three regions of Kazakhstan, namely, Astana City, Kokshetau City, and Akmola Oblast (district). The participants’ work experience ranged from 2 to 20 years and included 82 female (79.6%) and 21 male (20.4%). The data were collected via a Google Form questionnaire and interviews. The data were analyzed descriptively using frequency and percentage gained from the questionnaire and interpretation of interview results. All results were analyzed qualitatively and quantitatively. The result has confirmed that the Kazakhstani in-service English as a foreign language teacher is aware of differentiated learning as an effective strategy in catering to students’ differences in the classroom. The results of the study have shown that 90% of Kazakhstani English as a foreign language teachers who participated in this study support the idea of using the differentiated learning approach in heterogeneous classrooms, as they agree that differentiated learning contributes to an increase in motivation and quality knowledge and make the English language learning enjoyable and accessible; however, lesson planning and preparation of study materials for differentiated learning in heterogeneous class is troublesome. The study discovered that a considerable number of English as foreign language teachers still struggle in applying the differentiated learning approach due to some factors such as large workload, lack of knowledge, insufficient training, large class sizes in the class, shortage of funding, and shortage of facilities for differentiated instruction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".