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Record W2017575458 · doi:10.5539/ijps.v2n1p89

The use of Study Anxiety Intervention in Reducing Anxiety to Improve Academic Performance among University Students

2010· article· en· W2017575458 on OpenAlexvenueno aff
Prima Vitasari, Muhammad Nubli Abdul Wahab, Ahmad Othman, Muhammad Ghani Awang

Bibliographic record

VenueInternational Journal of Psychological Studies · 2010
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
FundersUniversiti Malaysia Pahang
KeywordsAnxietyIntervention (counseling)PsychologyClinical psychologyControl (management)Medical educationApplied psychologyPsychiatryMedicineComputer science

Abstract

fetched live from OpenAlex

Anxiety is one of the wide varieties of emotional and behaviour disorders, it is a major predictor of low academic performance. To this, anxiety should be taken seriously. Students needed some intervention to reduce anxiety in improving academic performance. Study anxiety intervention is designed to help students handling the problem regarding of their study process. In this paper, the study anxiety intervention to manage study anxiety in order to increase academic performance among students is proposed. This research selects twelve participants. They are divided into two equal groups, the experiment and the control groups. The training runs on six sessions with the experiment groups received full training and no training of the control groups. The results show that the experiment groups perform better in reducing anxiety levels as well as increase academic performance than that the control groups. Based on these results, study anxiety intervention can be concluded as an effective program to improve academic performance among university students. Therefore, the participants should be practiced the techniques to get the intensive level in mastering regarding of managing study anxiety and improving academic performance

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 score0.069
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.063
GPT teacher head0.386
Teacher spread0.323 · 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

Citations51
Published2010
Admission routes1
Has abstractyes

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