Teaching International Students to Analyze Textual-Discursive Categories
Bibliographic record
Abstract
The purpose of the study is to identify how the course that covers the components of the ten-stepwise approach to discourse analysis of political texts helps international students study the political meanings in Ukraine. The study used the structured observation method to collect rather quantitative than qualitative data and observers’ reports on the sampled students’ performance in the in-class and out-of-class assignments. It also used discourse analysis awareness test, observation report checklist, and assessment checklist to yield the quantitative data. The course that is based on the ten-stepwise approach to discourse analysis of political texts proved to raise the students’ overall awareness of analysis of textual-discursive categories and fosters their skills of both discourse analysis and technical skills to use the NVivo 12 software tool. The results of the Discourse Analysis Awareness Test showed that the sampled students’ awareness of discourse analysis was generally good. The mean values varied between 0.643 and0.857, which corresponded to 65-85 grades ECTS. The analysis of the observation reports showed that the five most frequent words used in the corpus of the observation reports of seven experts were as follows: students, contributed, equally, succeeded, managed. All of them evoke a positive idea and feeling and reveal success in meeting goals. The quotes yielded from the reports implied that the course sessions were engaging, challenging, and fruitful in terms of learning how to analyze textual-discursive categories found in political texts. The descriptive statistics drawn from the observation checklist and presented by course topic showed that the observers’ mean values improved throughout the course sessions that meant that the students progressed in the discourse analysis.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".