Factors Influencing Adaptation Of Indigenous Students At Selected Public Universities In Malaysia
Bibliographic record
Abstract
Despite an increase in access to tertiary education, poor performance and high dropouts among the indigenous students are still evident. Past studies from Australia, Canada and New Zealand show that indigenous students’ failure to adapt to academic and social life in the campus was a contributing factor. However, study on indigenous students in the local university context is still scarce, hence, not much is known about their state of adaptation. To fill in the literature gaps, this study aims to determine the level of adaptation among indigenous students at public universities in Malaysia and explore whether their adaptation differs according to demographic profile. A Mixed Method Sequential Explanatory Design was carried out to achieve the objectives of the study. Data was collected through both quantitative and qualitative approaches. Quantitatively, 160 (n=160) indigenous students from 10 public universities were sampled through purposive sampling. Their adaptation was measured by the Self-Adaptation to College Questionnaire (SACQ). Instruments that were used to measure factors related to indigenous’ adaptation were self-esteem scale, resiliency scale, self-regulation scale and the support scale. Qualitatively, 12 indigenous students have participated in semi-structure interviews to explore the challenges faced by them when studying at the university and to understand the sources of the support that they received. The findings revealed that indigenous students’ have moderate level of academic adaptation (m=2.93), high level of socio-emotional adaptation (m=3.07) and high overall adaptation (m=3.02).Level of adaptation also differ according to their demographic such as first-generation, ethnicity and household income.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".