Iranian Student’s Emotion in Government University in Malaysia
Bibliographic record
Abstract
Learning situations in modern society are getting increasingly complex and variable, and learners have to take more responsibility for their own learning. The main purpose of this study was to understand Iranian student’s feelings, who studying in selected Government University in Malaysia. The study was carried out through three research questions: 1) How do the Iranian student’s feel about life in Malaysia? 2) How these feelings do affects on Iranian student’s educational progress? 3) What are the factors that contributed to these feeling? Due to the nature of study, a qualitative research method and techniques was used to enable the researcher to understand emotion of Iranian student whose study at one of Government University in Malaysia. Data was gathered from interview with 3 Iranian students via “convenience sampling”. “Constant Comparative” method was used for data analysis. Eight major themes (worry, sad, happy and comfortable, socio-culture factors, economic factors, and good relationship and environment facilities) emerged from this study in relation to Iranian student’s emotion in selected Government University in Malaysia. This study concludes that based on the findings, graduated student organization can designed intervention program base on International students’ views in their social, cultural and economical content.Key word: Student’s emotional, Learning strategy, Malaysia
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".