Employability in online higher education: A case study
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
<p>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.</p><p>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<span class="apple-converted-space">. 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.</span></p>
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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 | 0.008 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it