Modelo predictivo en comprensión de lectura impresa y digital en estudiantes universitarios
Bibliographic record
Abstract
Introduction: Reading comprehension (CL) is complex and in its process, faculties related to perception, motivation, attention, and interpretation are involved. Our goal was to design a predictive model in understanding printed and digital reading, based on the Theory of the six readings, in students from the universities of Manizales, Colombia and Kharkiv, Ukraine. Methods: During a period of 6 weeks, 70 students participated: Colombians (n=40) and Ukrainians (n=30), in which intervention was generated with the "six reading theory" course together with cognitive evaluation with the MoCA Test (Montreal Cognitive Assessment) instrument. Data were analyzed by descriptive statistics for categorical and numerical variables, multivariate analysis and prediction by multinomial regression techniques. Results: The intervention group that received training in the Theory of Six Readings generated better overall results in reading comprehension, a situation that correlated with the predictive model (r2=0.67; global classification index = 0.76). Conclusion: Implementing a print and digital reading program based on the theory of the six readings can allow the optimization of reading proficiency in public and private universities.
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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".