Role of Research-based Learning on Graduates’ Career Prospects
Bibliographic record
Abstract
Education is still a leeway towards achieving individual’s personal growth as well as professional development. Further and Higher Education (FHE) are even more crucial in accelerating the achievement of these goals. Consequently, graduate students explore endless opportunities to enroll for postgraduate programs, hoping to gain financial independence, economic freedom, and improved standard of living after completion. Since graduate programs offer such tremendous career and life-changing opportunities, it is imperative to investigate if programs like the master’s in business administration are still relevant in today’s fast-moving business environment. This phenomenology study systematically utilizes underlying assumptions of research-based learning to assess a core aspect of universities’ MBA curriculum, that is writing a dissertation. It examines the value added by dissertation to graduates’ long-term career goals. Data for the study was obtained from fourteen MBA graduates through unstructured in-depth interviews. All the graduates currently work as full-time employees in their respective organisations, who were drawn from four main departments namely marketing, education, accounting and the IT industry. Our findings are thought provoking, yet compelling, in the sense that participants expressed mixed opinions concerning whether the dissertation prepared them for their current job roles. Most of them attributed their career successes to luck and hard work. Good communication and leadership skills also played major roles. Only few of them did acknowledge honing such skills while writing their dissertation during the research process. The implication of this research to stakeholders of higher education institutions, and policy makers, are also discussed.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".