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Record W2788252806 · doi:10.5539/ijel.v8n3p131

Metacognitive Scaffolding in Reading Comprehension: Classroom Observations Reveal Strategies to Overcome Reading Obstacles of Engineering Students at QUEST, Nawabshah, Sindh, Pakistan

2018· article· en· W2788252806 on OpenAlexvenueno aff
Mansoor Ahmed Channa, Zaimuariffudin Shukri Nordin, Abdul Malik Abassi

Bibliographic record

VenueInternational Journal of English Linguistics · 2018
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsMetacognitionMathematics educationReading comprehensionSession (web analytics)Class (philosophy)ComprehensionReading (process)PsychologyThink aloud protocolComputer sciencePedagogyCognitionArtificial intelligenceLinguistics

Abstract

fetched live from OpenAlex

This study aimed at investigating the development of reading comprehension of engineering students through metacognitive strategies and scaffolding. This study used 12 classroom observations in four engineering departments of one public university in Pakistan. The researcher observed 3 classes in each department at the time of read-aloud sessions. The class in each department was comprised on minimum 55 students and maximum 75 students. The researcher himself conducted all the 12 observations to maintain reliability without interfere of the complete teaching method. Teacher in each class was introduced by the observer and his aim to come in the first observation session. The observer sat at the back of every classroom and noted all instructional practices carefully on the field-notes based on teachers using metacognitive strategies to support students in terms of reading comprehension instructions. This study revealed the promising results based on metacognitive scaffolding and strategies as the most important tools for engineering students and language teachers to use for the development of reading and comprehension.

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)0.003
metaresearch head score (Gemma)0.036
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.410
Teacher spread0.357 · 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 designObservational
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

Citations2
Published2018
Admission routes1
Has abstractyes

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