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Record W2481444104 · doi:10.5539/ies.v9n8p127

The Relationship of Mental Pressure with Optimism and Academic Achievement Motivation among Second Grade Male High School Students

2016· article· en· W2481444104 on OpenAlexvenueno aff
Ali Sedigh Sarouni, Hossein Jenaabadi, Abdulwahab Pourghaz

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsOptimismPsychologyAcademic achievementDescriptive statisticsRegression analysisPearson product-moment correlation coefficientDevelopmental psychologySocial psychologyStatisticsMathematics

Abstract

fetched live from OpenAlex

<p class="apa">The present study aimed to examine the relationship of mental pressure with optimism and academic achievement motivation among second grade second period male high school students. This study followed a descriptive-correlational method. The sample included 200 second grade second period male high school students in Sooran. Data collection tools in the current study were the Ursula Markham Mental Pressure Inventory (1976), the Tschannen-Moran et al. Optimism Scale (2013), and the Hermans Academic Achievement Motivation Questionnaire (1977). The obtained data was analyzed using both descriptive and inferential statistics (Pearson correlation coefficient and regression analysis) via SPSS software. The results indicated that mental pressure was significantly and negatively related to optimism (P<0.01), such that with an increase in mental pressure, students’ optimism decreased. The results of regression analysis revealed that mental pressure predicted 5% of the variance in students’ optimism. Moreover, mental pressure was significantly and negatively related to students’ academic achievement motivation (P<0.01), such that with an increase in mental pressure, students’ academic achievement motivation decreased. The results of regression analysis revealed that mental pressure predicted 4% of the variance in students’ academic achievement motivation.</p>

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)0.000
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 score0.049
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.045
GPT teacher head0.381
Teacher spread0.336 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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