The Case Studies as an Efficient Method for the Formation of Students’ Multicultural Competence
Bibliographic record
Abstract
The article describes the features of students’ multicultural competence formation with the case study method. Presented an analysis of modern psychological and pedagogical approaches in teaching English of students. Described concepts and types of case technologies used in teaching English. Analyzed the system of work in teaching English-speaking high school students by means of situational tasks. Stressed, that Case technology solves complex of some problems: develops communication skills, helps to establish emotional contacts between students; solves the content and information problem, as they provide students with the necessary information, without which it is impossible to carry out joint activities; develops special skills of critical thinking (analysis, synthesis, goal setting etc.), provides solutions to educational problems, a learning task, as students are taught to work in a team, listen to other people's opinions. Analyzed methodological recommendations, techniques, methods and pedagogical conditions of students' work with case technology in the process of learning English speaking helped to expand the range of speech genres, including appeals, requests, explanations and more. Explained students skills of talking to an opponent, colleagues, presentation speech, dialogue monologue and more as multicultural competence.
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".