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Record W2060916510 · doi:10.5539/ass.v6n5p100

The Relationship between Test-Anxiety and Academic Achievement among Iranian Adolescents

2010· article· en· W2060916510 on OpenAlexvenueno aff
Fayegh Yousefi, Mansor Abu Talib, Mariani Mansor, Rumaya Juhari, Ma’rof Redzuan

Bibliographic record

VenueAsian Social Science · 2010
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
FundersUniversiti Putra Malaysia
KeywordsAcademic achievementAnxietyPsychologyTest anxietyTest (biology)Clinical psychologySignificant differenceMental healthAchievement testDevelopmental psychologyMedicinePsychiatryStandardized testMathematics education

Abstract

fetched live from OpenAlex

The purpose of this study is to determine the relationship between test-anxiety and academic achievement among adolescents in Sanandaj, Iran. The respondents comprised of 400 students (200 boys and 200 girls) in the age range of 15-19 years old that were randomly selected from nine high schools in Sanandaj, Iran. A self administered questionnaire was used for data collection which includes a Test-Anxiety Inventory (TAI) (Abbolghasemi, 1988), Grade Point Average (GPA) score and personal information. Result shows that there is a significant correlation (r= -0.23, p=.000) between test anxiety and academic achievement among adolescents. In addition, there is a significant difference (t= 5.47, p=.000) of academic achievement between male and female adolescents whereby female score higher in their academic achievement. It is recommended that academic achievement and mental health be improved in school settings with support strategies such as educational guidance, counseling and psychotherapy or other psycho-educational program such as teaching life skill.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.319
Teacher spread0.288 · 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

Citations45
Published2010
Admission routes1
Has abstractyes

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