The Role of Motivation among Heritage and Non-Heritage Learners of Russian
Bibliographic record
Abstract
Because motivation plays a major role in the development of language proficiency, it is important to understand what motivates learners. Unfortunately, instructors of heritage languages are not often aware of the specific motivations driving their students. This article considers motivation as an ‘integrative orientation’ and as an ‘instrumental orientation’ to understand heritage speakers who enroll in Russian language courses. My research project was devised to identify the motivations of Russian language learners and to compare the motivations of heritage learners with those of non-heritage learners. With this purpose in mind two questionnaires were applied: one that comprises a section of a placement test used in heritage learner classes; the other consisting of a formal questionnaire distributed among forty learners of Russian in two North American universities. My article discusses the results of both surveys as well as their implications for classroom use and further research. The findings of this study might encourage Russian language instructors to reexamine how their own students’ motivational factors affect their own language development and how the curriculum addresses students’ needs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".