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Record W2898232405 · doi:10.5539/ass.v14n11p1

Anxiety, Motivation and Attitude of Indian Students in Learning English in National-Type Tamil Schools

2018· article· en· W2898232405 on OpenAlexvenueno aff
Sinusha A.P. Murthy, Kee Jiar Yeo

Bibliographic record

VenueAsian Social Science · 2018
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsTamilPsychologyAnxietyEnglish languageSocioeconomic statusNeed for achievementPositive attitudeMathematics educationSocial psychologyMedical educationDevelopmental psychologyDemographySociologyPopulationLinguisticsMedicine

Abstract

fetched live from OpenAlex

The aim of this study was to determine the students’ anxiety, motivation and attitude in learning English based on their socioeconomic status and English language achievement in National-type Tamil schools. Samples for this study comprised of 144 year 5 Indian students from four Tamil schools. Data for this study were collected by questionnaire adapted from instruments by Tsai & Chang (2013), Ghazvini & Khajehpour (2011) and Gaur (1985). The findings of this study showed that Indian students from urban and rural areas have a moderate level of anxiety in learning English. Students from both areas were instrumentally motivated and showed positive attitudes towards learning English. However, negative correlations were identified between English Language achievement and level of motivation in learning English as well as between daily spoken languages at home and with friends and the level of English language achievement. The result of this study also illustrated that level of motivation and attitude are positively correlated. In conclusion, the samples of this study showed high levels of anxiety as well as motivation and attitude in learning English. It is recommended that future research take more samples and include qualitative data to increase the reliability of the study.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.100
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.034
GPT teacher head0.384
Teacher spread0.350 · 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 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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