Vpliv funkcije samodejnega popravljanja v programih za urejanje besedil na akademsko pisanje učencev in učenk angleščine kot drugega tujega jezika
Bibliographic record
Abstract
The intrusion of technology into language education is undeniable. However, its impact on English as a Second Language (ESL) learners remains unexplored. This study explores how the text-processing and suggestion features of Microsoft Word affect the English language development of ESL learners. The writing samples show that while beginners make fewer spelling and punctuation errors, prolonged reliance on software weakens long-term language proficiency. This finding is supported by cluster analysis of first-year undergraduates, third-year undergraduates, and postgraduates. Conversely, first-year undergraduates learners excel in structuring paragraphs and writing a variety of sentences, which are the areas untouched by automation offered in the tested software. Semi-structured interviews with research-active academics and postgraduate students further validated these findings, highlighting a critical decline in writing confidence due to over-dependence on emerging technology. The study underscores the hidden costs of convenience, urging a recalibration of technology-integrated language pedagogy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".