A Comparative Analysis of Text Difficulty in Slovak and Canadian Science Textbooks
Bibliographic record
Abstract
Abstract One of the main purposes of textbooks is the mediation of educational content to students. The factual accuracy of information, as well as the clarity of the text for students plays a crucial role in this aspect. The inadequate text difficulty can complicate students' learning. Comparing different approaches to the text in textbooks, considering the objectives of education, represents key knowledge for teaching materials innovation. This research was therefore focused on the comparison of the Slovak and Canadian science textbooks for lower secondary education. The methodology for assessing text difficulty according to Nestler, Prucha and Pluskal was used for this purpose. The samples of text from the textbooks for 6th and 8th grade of lower-secondary school were assessed. It was found that the text in Slovak textbooks is significantly more difficult. While from the syntactic difficulty point of view differences were rather partial, the significant differences were found in the semantic difficulty of the text. The Slovak textbooks are burdened with an excessive number of professional terms. Considering the results in measuring scientific literacy, this approach to the text in the Slovak textbooks is not effective. The results obtained are therefore an incentive to revise used educational materials.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".