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Record W2114684628 · doi:10.5539/ies.v8n5p111

English for Teaching and Learning of Science and Mathematics in Malaysian Schools: A Comparative Study on Perception between Different Ethnics

2015· article· en· W2114684628 on OpenAlexvenueno aff
Mohd Arip Kasmo, Abur Hamdi Usman, Fazilah Idris, Mohamad Mohsin Mohamad Said, Nor Afian Yusof, Hamdzun Haron, Azizi Umar

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
FundersStrong
KeywordsLikert scaleMathematics educationStatement (logic)PerceptionPsychologySet (abstract data type)Science educationProblem statementScale (ratio)MathematicsDevelopmental psychologyComputer scienceGeography

Abstract

fetched live from OpenAlex

This paper reports a study on the perception of the people toward the teaching and learning of Science and Mathematics using English. Altogether, 641 respondents obtained randomly from all over Malaysia participated in the study. The respondents, male and female from the age of 20 to 55 were given a set of questionnaire, containing statements on various issues of Science and Mathematics. Each statement is accompanied with five choices of responses in the form of Likert type scale ranging from 1. Strongly disagree, 2. Disagree, 3. Not sure, 4. Agree, and 5. Strongly agree. One of the statement of the questionnaire analysed was “It is easier to learn science and mathematics in English”. The raw data was analysed using the Statistical Package for the Social Sciences (SPSS) and also ANOVA. The result of the study shows that 45.7% of the respondents rejected the statement, 24.4% accepted the statement and 29.3% were not sure. There is significant difference of means between the respondents with academic background in science compared to respondents with non-science academic background.

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)0.002
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.206
GPT teacher head0.500
Teacher spread0.294 · 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 designQualitative
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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