Metacognitive reflection and digital skills-based reading training in \nFrench minority language schools
Bibliographic record
Abstract
This study investigated students’ reading performance and how it relates to the use of a \ndigital reading training program combined with the use of metacognitive reflection. The \nparticipants were five grade 8 students, aged 13 and 14 years old, at a French minority language \nschool in Newfoundland. The data were collected using the following instruments: a demographic \nquestionnaire, a pre- and post-test reading performance assessment, the ELSAweb reading skills \ntraining program, and a metacognitive reflection questionnaire. The results were analyzed by \ncomparing reading speed before and after the study, computing growth scores for reading \ncomprehension before and after the study, and descriptive statistics to measure the outcomes of \nthe ELSAweb training program and metacognitive reflection questionnaire. The results of the study \nsuggest that there is a positive relationship between students’ reading performance and their use \nof the ELSAweb training program simultaneously with the metacognitive reflection questionnaire. \nThe results of the metacognitive reflection questionnaire revealed varying levels of engagement \nfrom students and demonstrated that those students who engaged more thoroughly with the \nmetacognitive reflection questionnaire obtained more significant results. The findings of this study \nare informative both at the school board level and within the classroom. The findings suggest that \nresearch-based reading training interventions are effective and should be incorporated into \ncurriculum in order to support students as they learn to read. Additionally, it suggests to teachers \nthat metacognitive reflection is a useful tool to equip students with as they are learning and building \non their new and existing skills.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".