The Transition of Higher Education for Continuous Lifelong Learning: Expert Views on the Need for a new Infrastructure
Bibliographic record
Abstract
In the contemporary need for continuous upskilling and reskilling, higher education has an important role to play. While the traditional university programmes are designed for students in their early twenties our knowledge society has a demand for lifelong learning in a wider age span. This paper is a part of a Delphi study on the ongoing transformation of higher education for lifelong learning. A qualitative Delphi study has been carried out in the four steps of 1) A literature study to explore the chosen topic, with the selected publications sent out to an expert panel, 2) A survey with questions to the experts based on the findings in the literature study, 3) Email interviews to dig deeper into the answers from the survey, and finally 4) Focus group interviews. The aim of the paper is to analyse, present and discuss the international expert panels' views on the infrastructural needs in the transformation of higher education. Data gathered from the three first steps, with a focus on the email interviews, have been analysed according to the Grounded Theory concepts of open, axial coding and confirmatory coding. The categories from the Open coding analysis were later, in the axial coding, grouped around the central axis of 'Higher education transformation for lifelong learning'. The confirmatory coding found the common denominator of 'Infrastructure', and its interrelationships with the attributes of 'Multimodal delivery', 'Pedagogical change', 'Quality and organisation', 'Equity, diversity and inclusion', 'Digital literacy', 'Accessibility', and 'Financial aspects'. Findings align to the Anna Karenina principle in the sense that a happy and healthy infrastructure for continuous lifelong learning in higher education, depends on all the attributes listed above. This leads to the Tolstoyan conclusion that every variation of failing attributes would result in its own state of unhappiness.
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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 | 0.038 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".