The “Digital Generation” is Learning to Read: Linguistic Factors of Eye Movement Parameters of Russian Schoolchildren of the 1st – 3rd Grades
Bibliographic record
Abstract
The level of reading literacy of the modern "digital generation" is an acute and significant topic. To explore this problem in dynamics it is necessary to objectively record the parameters of reading skills development in modern children. The article presents the results of an experimental study of the reading mechanism of elementary school students, performed using eye tracking. Fifty-three pupils in grades 1–3 of Moscow schools participated in the experiment, with real texts from Russian textbooks as stimulus material. Eye movements were recorded while reading texts from the screen, and after each text a comprehension question was asked. The results indicated a direct correlation between oculomotor characteristics and reading skill. From grade 1 to grade 3, the duration and number of fixations, amplitude duration, and reading time for both word and letter decreased, while the number of words with one or missing fixation increased. There was also a grade-independent effect of word length and word frequency factors on reading speed and oculomotor activity for students in all grades. Both factors had a significant effect on reading time, the average fixation duration was more sensitive to the frequency factor than to the word length factor, while word length alone influenced the first fixation duration.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".