Enhancing business undergraduates’ skills and future employability through career development and management \n
Bibliographic record
Abstract
There is currently a gap between employer expectations of the skills graduates should possess on entry to the workforce and the skills that new graduates possess. The new knowledge economy, emerging as a result of technological advancement, needs business graduates with flexible mindsets and transferable skill sets, capable of innovating and adapting to dynamic work environments. Many Australian companies are unable to attract competent and quality workers, with business leaders often citing poor business acumen, lack of relevant skills and real world experience as serious shortcomings leading to employers deeming new graduates as not work-ready. \n \nFor universities to stay relevant, they will have to be proactive rather than reactive, challenge existing pedagogies and re-examine their teaching approaches in higher education in order to add value to students’ learning and the community. To achieve this, course curricula must develop learning, teaching and assessment practices to encourage employability development to take place alongside developments in discipline specialisations. The importance of developing employability skills has been acknowledged by business, government and universities. Universities are now focusing on developing employability skills in students to prepare them for work in different work contexts and dynamic business environments (Barrie 2006; Bridgstock 2009). \n \n \nThe objective of this thesis is to study how one Australian university used a Career Development Learning activity to facilitate future employability preparation and development in first year business students in an Accounting course as part of careers education. Through personal student journal reflections, a majority of students found the experiential activity beneficial in helping them prepare for future employability. Through this activity, they learned about the skills required for successful careers and encouraged them to identify practical ways to improve their employability prospects. \n \n \nThe findings will be used to extend the Systems Theory Framework in Career Development and may assist academics and career practitioners to better prepare their business students to seek suitable post-graduation employment, thereby assisting to narrow the employer-graduate expectations gap.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".