Multimodal Interaction in a Foreign Language Class at Higher Education Institutions of Ukraine
Bibliographic record
Abstract
Multimodality is implemented to the modern learning environment in line with trends towards multidisciplinarity. In the current study, multimodal interaction is based on the mutual integration of understanding of multimodality in philological and pedagogical perspectives. The purpose of the article was to analyze and compare the results of learning a foreign language (German) for professional purposes (German for Economists) with an emphasis on multimodal interaction and without it (in a way of traditional language learning with a predominance of classical methods of classroom and extracurricular activities). There were universal scientific and specific methods used: a controlled-type educational experiment; Likert-scale type questionnaire; reliability test: Cronbach’s alpha using IBM SPSS Statistics 28.0.0.0; qualitative-quantitative interpretation and contrastive-comparative analysis of the obtained experimental data; statistical-mathematical interpretation of empirical data; comparative analysis; the functional analysis. Respondents of empirical intelligence were students of the Faculty of Management and Marketing, specialty 073 “Management”. Averagely in the experimental group, almost all the assessing criteria of the effectiveness of multimodal interaction outreached 4 points. These data were also confirmed by the results of self-reflection-questionnaire. The novelty of the research is in the principle of theoretical substantiation and practical application of the content of multimodal interaction as an umbrella term that integrates the most fundamental concepts of modern pedagogy in general and, in particular, methods of teaching a foreign language.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".