"WHAT WERE YOU THINKING?": THE USE OF METACOGNITIVE STRATEGY DURING ENGAGEMENT WITH READING NARRATIVE AND INFORMATIONAL GENRES
Bibliographic record
Abstract
This qualitative case study illustrates and compares the metacognitive strategies that a grade ‐ 3 female student used while reading narrative and informational texts. Data were collected from interviews, observations, and videotaping of the participant ʹ s narrative and informational text oral reading sessions and examined using thematic analysis. Findings showed that she used markedly different metacognitive strategies for each genre, resulting in comprehension difficulties while reading the inform ‐ ational text. This article suggests that for students to meet the challenges of inform ‐ ational texts, they must be taught specific metacognitive strategies while working with explicit text patterns. Key words: metacognition, comprehension, text, stimulated recall, self ‐ regulation Cette etude de cas qualitative illustre et compare les strategies metacognitives utilisees par une eleve de 3 e annee en lisant des textes narratifs et informatifs. Les donnees, provenant d’entrevues, d’observations et de videos des seances de lecture a haute voix de ces textes par la participante, ont fait l’objet d’une analyse thematique. Les resultats indiquent que l’ecoliere avait recours a des strategies metacognitives nettement differentes pour chaque genre de textes, ce qui entrainait des difficultes de comprehension pour les textes informatifs. Il semble donc que, pour que les eleves soient en mesure de saisir les textes informatifs, il faut leur enseigner des strategies metacognitives particulieres tout en tenant compte de la structure explicite du texte. Mots cles : enseignement de la lecture, metacognition, comprehension, texte, rappel stimule, autocontrole.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".