Factors that Influence Program Selection and Its Relationship with Students’ Achievement in Pre-University Study
Bibliographic record
Abstract
The purpose of this study is to recognize the influencing factors in program selection in pre-university level and its relationship with student’s achievement. Competition among school leavers to further their studies at a higher level institution is getting tougher; making choosing a program offered by universities becomes a real challenge. After completing secondary school studies, most universities require students to complete a minimum of 1–2 years of post-secondary education before enrolment. Pre-university program is one of the options of post-secondary education. Since the curriculum of pre-university program is very comprehensive, students attending this program have to undergo various challenges throughout their academic sessions. Therefore, students are expected to have their own targets and focus prior to attending any of these studies. In this study, the researchers use a survey to investigate and analyze factors that influence students’ decisions in choosing a pre-university program and also the relationship between their decisions making with their academic achievements. The study reveals that all the factors listed significantly influence the students’ decision making in choosing a pre-university program. However, not all of these factors are taken into consideration in choosing a program that has a significant relationship with their achievement. Therefore, knowledge derived from this study is beneficial to the Ministry of Higher Education and any higher learning institutions which offer pre-university programs including the policy makers. The knowledge also allows policy makers to plan preemptive action to improve student’s performance.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".