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Record W2884900808 · doi:10.5539/ijel.v8n6p78

Reading Comprehension and Metacognitive Strategies in First Year Engineering University Students in Pakistan

2018· article· en· W2884900808 on OpenAlexvenueno aff
Mansoor Ahmed Channa, Abdul Malik Abassi, Stephen John, Jam Khan Mohammad, Masood Akhtar Memon, Zaimuariffudin Shukri Nordin

Bibliographic record

VenueInternational Journal of English Linguistics · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsSyllabusMetacognitionReading comprehensionReading (process)Mathematics educationComprehensionFocus groupPsychologyComputer scienceEngineering educationPedagogyLinguisticsCognitionEngineeringSociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.349
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

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