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Record W2525334106 · doi:10.5539/res.v8n4p62

Learning Processes and Academic Achievement among Omani School Students

2016· article· en· W2525334106 on OpenAlexvenueno aff
Marwa Alrajhi, Hussain Alkharusi, Said Aldhafri

Bibliographic record

VenueReview of European Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAcademic achievementAnxietyCognitionSample (material)Critical thinkingMathematics educationDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

This study examined the competing role of cognitive and affective learning processes used by students on predicting their academic achievement. The sample included 342 students from different schools in Oman (female=74.6%). The participants responded to a group of measures as part of a national study examining different learning variables. For the purpose of the current study, the participants’ data of six measures (i.e., critical thinking strategies, organizational strategies, memory strategies, intrinsic motivation, extrinsic motivation, and anxiety) were obtained. In addition, the students’ grade average point (GPA) using their reported grades were also used. The findings showed that out of the affective model, anxiety level and intrinsic motivation were significant predictors of students’ academic achievement. However, critical thinking was the only predictor of academic achievement from the cognitive model.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

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

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 head0.071
GPT teacher head0.415
Teacher spread0.344 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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