On Becoming an Effective Mentor in Adult Education—Investigating the Perceptions of Greek Adult Educators
Bibliographic record
Abstract
Interest in mentoring, which is an innovative method in adult education (CEDEFOP, 2013), has been growing rapidly in Europe, especially since the Lisbon European Council in 2000. In Greece, this interest has found expression either through the development of educational material in the context of adult educator training programmes, or the investigation of the mentoring needs of adult learners (Koutsoukos, 2021). In spite of the obvious fact that the mentor plays a key role in ensuring that the mentoring process is constructive and successful, there has been little research to date on the attributes of an effective mentor in adult education. The present study, using multimethod research, examined the perceptions of 337 Greek adult educators as to what characteristics constitute an effective mentor, as well as the role and the selection criteria of a successful mentor. The findings indicated that the key qualities of an effective mentor were: having sufficient training in adult education and mentoring, teaching, communication and relational skills, as well as having a positive attitude to lifelong learning and a willingness to innovate. In addition, the role of an effective mentor was perceived by the study participants as being a trainer, a model teacher, as well as an equal partner.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".