Work Integrated Learning at Tertiary Level to Enhance Graduate Employability in Bangladesh
Bibliographic record
Abstract
In the last few years, higher education institutions (HEIs) in Bangladesh have been under severe pressure to transform the way they operate. The present Government of Bangladesh requires all universities to improve their quality of education and has implemented various projects such as Higher Education Quality Enhancement Project (HEQEP) in collaboration with the World Bank. As Bangladesh has set a target to transition out of the status of Least Developed Country (LDC) to Developing country by 2024, graduate employability and education quality are pivotal interests for the Government of Bangladesh. This paper investigates the concept of work integrated learning (WIL) and generic skills vital to enhance the employability of the current graduates in Bangladesh. We explore different types of WIL that can be applied at higher academic institutions in Bangladesh and what are the various benefits of this type of learning to the key stakeholders, namely; students, universities and employers. Finally, we conclude by pointing out the prerequisites that need to be considered in order to successfully implement WIL in Bangladesh. This is a descriptive study and we have collected data from different secondary sources such as documents available from government agencies, research organizations, archives and library. Moreover, we have also used interviews from sources such as newspapers and magazines documenting views of well-respected academicians and personalities in Bangladesh. Our findings indicate that in order to successfully integrate WIL, there are some prerequisites such as modifying the current curriculum, designing and offering work oriented courses, building strong connections with potential employers and creating awareness about WIL among faculty members and students.
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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 | 0.002 |
| 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 | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".