Learning the Russian Language in the Game: Traditional and New Approaches
Bibliographic record
Abstract
The article deals with the traditional game approaches that have well recommended themselves at the lessons of the Russian language, and their potential and ways of modifying into a single game space of the lesson is being discussed. Basing on personal experience, the authors of the article present the possibilities of organizing a Russian language lesson in the form of a quest. Many experts rightly paid attention to the effectiveness of using games in the learning process. Despite the attractiveness for teachers and students, until recently, game approaches as a form of education have remained on the periphery of the educational process, being just a supplement to the main methods. Only role-playing games can be called an exception, with their being included both in the educational process of school and university education, and in professional-oriented training of specialists. However, under the influence of processes in modern culture and the active development of gaming technology, the "gamification" of education acquires the character of a mass phenomenon both at school and in higher educational institutions, and ignoring these processes is not only impossible but impractical. In this regard, the article provides a scientific and methodological understanding of this form of education and identifies the structural peculiarities of the quest unlike the other game forms. The article is addressed to teachers of Russian as a foreign language and can be used as a kind of model for conducting quests in classes both in various courses on grammar, reading, writing, listening, linguistic and cultural studies, and in students' independent educational activities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".