International Student’s Academic Achievement: Contribution of Gender, Self-efficacy and Socio-cultural Adjustment
Bibliographic record
Abstract
International students who pursue their education in other country have to cope various challenges related to their adjustment. Successful adjustment to higher learning institution becomes an important issue, failure to which may hinder self-efficacy and their academic achievement. Aim of current research is to investigate the influence of self-efficacy, and socio-cultural adjustment on their academic achievement among International students in Malaysia. Two hundreds International students were randomly selected from the Kuala Lumpur, Out of these 100 belongs to male and 100 belong to female category. The age range of the respondents are 17 to 27 years (Mean=20.55). Various psychological constructs were used to assess the studied variables. Correlational and regression statistical analysis were applied. Findings reveal significant positive correlation in academic achievement with socio-cultural adjustment and self-efficacy. Gender, self-efficacy and socio-cultural adjustment significantly predicts students’ academic achievement. Findings proved gender, self-efficacy and socio-cultural adjustment significantly effecting International student’s academic achievement among International students in Malaysia. Implications for International students are significant in terms of countering adjustment problems by developing self-efficacy and cultural values as a result academic achievement may improve.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".