Designing of Questionnaire for Factors that Impact Student Learning Outcomes in Tertiary Education System: An Analysis from Pakistan
Bibliographic record
Abstract
The current research focused on the designing of questionnaire for factors that impact student learning outcomes in tertiary educational system in underdeveloped nation Pakistan. A pilot study was conducted for the designing of questionnaire to collect data on perceived factors that impact student learning outcomes. The selected Higher Education Commission (HEC) recognized private educational institutions were contacted and permission of data collection was obtained from the authorities. Ninety eight students of final semester from HEC recognized tertiary universities at Bachelors level from the department of Science were approached for this study. Participants were informed that the data will be kept in a laptop under password protection. There were 41 total items in the questionnaire. It has been divided into 2 sections. Section A includes the demographic characteristics of participants and includes 7 questions. Section B is divided into 6 subsections, each section capturing the true essence of independent and dependent variables. The results of the study concluded that student perception questionnaire has good to excellent intra rater reliability and indicates that all components have factor loading exceeding 0.50 that also shows the importance of all components. Thus, the questionnaire can be used for the assessment of factors that impact student learning outcomes from educational institutes.
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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 | 0.005 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".