Korean Learners’ Metacognition in Reading Using Think-Aloud Procedures with a Focus on Regulation of Cognition
Bibliographic record
Abstract
The primary goal of this study was to explore the changes that four Korean university students made in their regulation of cognition during reading processes. The students were trained using explicit reading strategy instruction based on the CALLA model. To this end, first, metacognition was framed and categorized by the definition from Baker and Brown (1984) and, second, a scoring scale for measuring the readers’ regulation of cognition was developed based on the study by Block (1992) to examine and trace any changes in their regulation processes. For data analyses, the participants’ think-aloud protocols were used. The results indicate that there were marked changes in the frequencies of their regulation processes over time. Specifically, the students’ overt strategic and regulatory behaviors in a regulation process showed more flexibility and organization toward the end of the strategy training. This study suggests that students would benefit from being provided with sufficient time for practice in order to build effective regulation of cognition in reading processes and that the teacher should understand the complex nature of the regulation processes that students go through. In addition, think-aloud procedures as an instructional tool for effective strategy training was shown to be a worthwhile technique in the classroom.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".