Role of Research-based Learning on Graduates’ Career Prospects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it