The Lifelong Learning Iceberg of Information Systems Academics - A Study of On-Going Formal and Informal Learning by Academics
Bibliographic record
Abstract
This article describes a study that examined the lifelong learning of information systems academics in relation to their normal work. It begins by considering the concept of lifelong learning, its relationship to real-life learning and that lifelong learning should encompass the whole spectrum of formal, non-formal and informal learning. Most world governments had recognised the importance of support for lifelong learning. Borrowing ideas and techniques use by Livingstone in a large-scale 1998 survey of the informal learning activities of Canadian adults, the study reported in this article sought to uncover those aspects of information systems academics’ lifelong learning that might lead policy setters to understand the sources of learning valued by these academics. It could be argued that in the past the university sector was a leader in promoting the lifelong learning of its academic staff, but recent changes in the university environment around the world have moved away from this ideal and academics interviewed from many countries all report rapidly decreasing resources available for academic support. In this environment it is important to determine which learning sources are valued by information systems academic so that informed decisions can be made on support priorities.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.023 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".