The Potential for the Development and Implementation of Blended Learning at the Universities of Kazakhstan
Bibliographic record
Abstract
This research paper centers its attention on analyzing the blended learning method used in educational system in America, Europe and Kazakhstan, its holistic concept, perspectives, potential, applications, effectiveness and implementation. To determine the efficiency of the blended learning, we conducted a survey among English teachers using comparative analysis, an interview, a survey, and a questionnaire which revealed that the opinions differ regarding to the essence of blended learning technology, frequency, difficulties, problems in use and their solutions within the framework of the students’ independent work (SIW) and students’ independent work with a teacher (SIWT). According to the study the most effective, frequently used models in teaching a foreign language are considered by teachers to be the “Face-to-face” model (20.4%), as well as the "Training with continuation" model (12.2%). We also conducted a pedagogical comparative experiment among students on the basis of a higher educational institution. Two groups of students were selected: control group and experimental group. The research showed that using this method increased the students’ level of English language skills (listening by 15%, reading by 18%, writing by 10%, speaking by 9%, and students’ motivation in learning boosted by 22%) during one semester. Overall, the findings derived from the investigation indicate a pressing demand for a pragmatic comprehension of the theoretical foundations of the blended learning in the specific context of higher education programs.
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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 | 0.006 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".