A Review on Outcome Based Education and Factors That Impact Student Learning Outcomes in Tertiary Education System
Bibliographic record
Abstract
Education opens numerous revenues to register economic expansion all around the world with specific reference to developing nations. Advancement of Pakistan in education indicators has been severely insufficient during the previous decades. Decreased financing along with inefficiency in budget expenditure plus weak management system have crippled the education sector ensuing poor educational outcomes. Outcome-based Education (OBE) has recently gained much attention in Pakistan. OBE is used in education because it clearly focuses and organizes everything in an educational system around what is necessary for all students to be able to do at the end of their learning. OBE proposes an influential and interesting option of transforming and organizing medical education. Therefore, the basic aim of this review is to highlight the tertiary education system of Pakistan and the need to shift from teacher centered to Outcome Based Education system. The review also addresses the major factors that impact student learning outcomes. Data bases were searched including Cochrane and Medline. Search strategy was designed by combining Boolean operators and key terms related to review objectives. Seven studies were included in the paper regarding the effectiveness of Outcome Based Education in different disciplines of education. The findings suggested five important factors from the literature that impact student learning outcomes including, assessment strategies, learning objectives based on level of complexity, student preferred learning styles, English language competency and Employer requirements. However, limitations were recognized in the methodology section and further recommendations were given for future researchers.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".