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Record W1970178076 · doi:10.5539/elt.v2n2p16

Motivation and Attitude in Learning English among UiTM Students in the Northern Region of Malaysia

2009· article· en· W1970178076 on OpenAlexvenueno aff
Samsiah Bidin, Kamaruzaman Jusoff, Nurazila Abdul Aziz, Musdiana Mohamad Salleh, Taniza Tajudin

Bibliographic record

VenueEnglish Language Teaching · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySignificant differenceMathematics educationSpearman's rank correlation coefficientTest (biology)CorrelationRank correlationAnalysis of varianceSocial psychologyStatisticsMathematics

Abstract

fetched live from OpenAlex

The purpose of this study was to describe the relationship between the students’ motivation and attitude and their English Language performance. The subjects were the part two students from three UiTM campuses in the Northern Region. The research tool used was in the form of a questionnaire. The mean scores analysis of the motivation and performance revealed that the students were more extrinsically than intrinsically motivated when learning English. However, the one-way ANOVA test showed that there is no significant difference. To test the students’ attitudes and their performance, the Spearman Rho Rank-Order Correlation Coefficient was used and the analysis revealed a significant correlation between attitude and achievement. The one-way ANOVA also showed that there is a significant difference. The mean scores was used to find out whether there is any difference between the respondents of different gender and their attitude in learning English. The results showed the existence of significant difference.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations47
Published2009
Admission routes1
Has abstractyes

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