Factors Supporting and Preventing Academics from Becoming Lifelong Learners
Bibliographic record
Abstract
In present circumstances, it has become inevitable for individuals to continue obtaining new information and skills throughout their lives. Having learned to learn and information literate individuals are able to meet their learning needs both in career and personal terms by themselves. The important aspect is the individual feels the need to learn and knows how and where to meet these needs. Feeling those can be considered as the basic requirement to make efforts in order to meet them. However, the surrounding circumstances may support or prevent meeting these needs. Therefore, the main questions to be answered in this study are as follows: (1) what are the factors supporting academics, holding office in the faculty of education, to become lifelong learners? and (2) what are the factors preventing academics, holding office in the faculty of education, from becoming lifelong learners? Since the situation of being a lifelong learner will be examined by being based on the present working conditions, the study is designed as embedded multiple case study. It is endeavored to ensure maximum variety in the study group by taking into account the various academic titles, gender and fields of study. The interview form is developed based on the literature and revised according to the opinions of specialists. The data has been collected by having individual interviews with academics and a content analysis has been performed. In conclusion of the analysis performed, such themes as the need to become lifelong learners, the factors supporting to become lifelong learners and the factors preventing from becoming lifelong learners have been reached.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".