Employability in online higher education: A case study
Bibliographic record
Abstract
Over the past 15 years, learning in distance education universities has become more interactive, flexible, collaborative, and participative. Nevertheless, some accounts have highlighted the importance of developing more instrumental and standardized educational practices to answer the challenges of employability. In fact, the choice of skills that are important to learning communities and the labour market has been the subject of controversy because it involves heterogeneous motives among different groups. This paper compares the perceptions of employability skills in a sample of teachers from the Universidade Aberta and a sample of students who attend a local learning centre at this University. The research focused on the following dimensions: a) the most important employability skills, and b) the employability skills to be developed in online undergraduate degrees. To collect the required data, a questionnaire was prepared and applied to students and teachers, taking the theoretical model of Knight and Yorke (2006) as its main reference. In spite of the specificity of each group, the results revealed some similarities between students and teachers with regard to employability. The conclusions also highlighted the need to promote research on this matter in online education.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".