En expertgrupps syn på livslångt lärande inom högre utbildning : Förändringsbehov och vidareutveckling
Bibliographic record
Abstract
Det finns ett behov av kontinuerlig kompetensförstärkning och här spelar högre utbildning en viktig roll. Medan de traditionella universitetsutbildningarna ofta är utformade för studenter i tjugoårsåldern, kräver dagens kunskapssamhälle ett livslångt lärande för ett vidare åldersspann. Utifrån denna utgångspunkt genomförde Peter Mozelius, Marcia Håkansson Lindqvist och Jimmy Jaldemark vid CER, tillsammans med Martha Cleveland-Innes vid Athabasca University i Kanada, en studie där resultat från enkäter och intervjuer med en internationell expertgrupp indikerar en rad olika förändringsbehov. I denna kortrapport presenteras de aspekter som framkom i form av en konceptuell modell. Forskningsresultaten har tidigare presenterats vid två internationella konferenser: “Digging deeper with Delphi: The four step Alberta approach” (Mozelius, Cleveland-Innes, Håkansson Lindqvist och Jaldemark 2023a) och “The transition of higher education for continuous lifelong learning: Expert views on the need for a new infrastructure” (Mozelius, Cleveland-Innes, Håkansson Lindqvist och Jaldemark 2023b) samt i tidskriftsartikeln ”Critical aspects of a higher education reform for continuous lifelong learning opportunities in a digital era” (Mozelius, Cleveland-Innes, Håkansson Lindqvist och Jaldemark 2024).
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".