The factors explaining reading success of academically gifted readers through the ecological model
Bibliographic record
Abstract
This study aims to discover the best appropriate model to explain reading success of academically gifted students through the ecological model. Three models (i.e., Model 1, Model 2, and Model 3) were created by using three layers of the ecological model to investigate the ecological background of reading success. In line with the literature, seven explanatory factors were examined among the items in the student questionnaire of PISA 2018. Exploratory factor analysis to detect factors and confirmatory factor analysis to validate them were used respectively. Cronbach’s Alpha values of each factor (internal consistency) were also calculated. Structural equation modeling was performed to create a model explaining reading success. Afterward, indices of goodness-fit-criteria were examined. The findings indicated that there is a complex background for reading. All factors (i.e., perception of difficulties, perception of competence in reading, enjoyment of reading, teacher support, teacher feedback, value of school and disciplinary climate in the classroom) have a significant effect on reading. According to the results, Model 3 has the best model fit indices among other models. This model, having more complexity and interaction among latent variables, was found as the most comprehensive and appropriate model due to being coherent with the ecological model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".