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Record W1510509520 · doi:10.5539/res.v7n7p453

Assessing Matriculation College Students’ Metacognitive Awareness Reading Strategies (MARS) in Biology

2015· article· en· W1510509520 on OpenAlexvenueno aff
Arsaythamby Veloo, Mariam A Rani, Rosna Awang Hashim

Bibliographic record

VenueReview of European Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsMatriculationReading (process)MetacognitionMathematics educationMars Exploration ProgramPsychologyAffect (linguistics)BiologyCognitionPolitical science

Abstract

fetched live from OpenAlex

This study examines the relationship between student’s perceived use of Metacognitive Awareness Reading Strategies (MARS) in reading Biology books and corresponding Biology achievement. This study also identified the effective reading strategies that affect students’ Biology achievement in a particular semester. This study selected 318 Biology students by random sampling which comprised 97 (30%) male and 221 (70%) female students who were studying in one of the Matriculation Colleges in Kedah state, Malaysia. This study use Metacognitive Awareness of Reading Strategies Inventory (MARSI) constructed by Mokhtari and Reichard (2002). Findings of this study show that there is a positive weak relationship between perceived use of MARS and their Biology performance in Matriculation Programme. This study also show that Global Reading Strategies and Problem-Solving Strategies are predictors but Support Reading Strategies is not a predictor of Biology achievement. In investigating the effects of strategies, Global Reading is the best contributor followed by Problem-Solving Strategies to predict Biology achievement. It is suggested that teachers learn how to practice reading strategy instruction in the classroom. Students themselves should learn MARS and apply them in their reading and focus more on Global Reading Strategies.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.735
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
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 head0.224
GPT teacher head0.524
Teacher spread0.300 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2015
Admission routes1
Has abstractyes

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