A Quantitative Research for Improving Reading Comprehension of First Year Engineering Students of QUEST, Pakistan Through Metacognitive Strategies
Bibliographic record
Abstract
This quantitative research investigates first year engineering students’ reading comprehension using the different metacognitive strategies and scaffolding strategies. The research was undertaken at QUEST, Nawabshah, Pakistan. The respondents of this research were taken from four engineering departments including Mechanical Engineering, Energy and Environmental Engineering, Electrical Engineering, and Computer System Engineering. A set of questionnaire was used among 311 respondents. The data was analyzed using descriptive statistics to analyze research variables through SPSS 17 for producing the Percentages, Mean and Standard Deviation of the data. The results acquired from data suggested that the engineering respondents used their metacognitive strategies in order to make their comprehension easy to apprehend the meaning of reading passages. This research also revealed the average uses of twenty important categories on metacognitive strategies as reported by engineering respondents. The mean score for ‘I often find that I have been reading for class but don’t know what it is all about’ category (M = 2.65) was rated by the respondents of this research as the highest; while the mean score for ‘reading instructions carefully before beginning a task’ (M = 1.54) was rated as the lowest. The results also showed that the respondents of this study revealed the average uses of the twelve important categories of scaffolding. However, the mean score for ‘When studying this course I often set aside time to discuss the course material with a group of students from the class’ category (M = 2.29) was the highest for all respondents; whereas, the mean score for ‘I ask teachers/students for help when they do not understand’ (M = 1.37) was the lowermost. However, no category of metacognitive strategies and scaffolding fell into low level of usage. To sum up, results are presented for developing effective reading strategies for engineering students to improve their reading proficiency.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".