Reading Comprehension and Metacognitive Strategies in First Year Engineering University Students in Pakistan
Bibliographic record
Abstract
The paper investigates the use of metacognitive strategies by first year engineering students at the time of classroom practice on reading text. The study was conducted in four engineering departments of a university in Pakistan. Data was collected through focus group interviews of first year engineering students. The researchers developed interview questions which were validated by two experts at university Malaysia Sarawak. Students were divided into 8 groups and each group had 5 informants. The data was recorded in audio-tape and organized gathered data through NVivo version 8 for interpretation of the results. The most important themes were generated through data analysis including thinking through images of the texts, selecting the main ideas, selecting the topic sentences, scanning of the texts, summarizing of the texts, and Questioning. The study contributed theoretically by giving the most promising results which showed that more than half of these groups used metacognitive strategies in classroom reading practice while less than half of groups did not use strategies and remained poor in reading comprehension. This study proposed to develop reading comprehension courses and syllabus based on reading strategies for engineering students.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".