BLENDED TUTORING: AN EXPLORATION OF TUTOR EMOTIONAL COMPETENCES VALUED BY LEARNERS IN A HIGHER EDUCATION CONTEXT
Bibliographic record
Abstract
This paper reports on research into the emotional competences that mature higher education (HE) students, working in blended learning contexts and studying part-time (PT), vocationally relevant, degrees within a School of Education, value in their tutors. A mixed methods approach was adopted to conduct a detailed exploration of eight tutors’ practice with data gathered from three principal sources. Interviews with tutors explored their approaches to delivery and considered factors that impacted on quality; students’ perceptions of their learning experiences were assessed using an attitude survey; and, an analysis of the content and communications in the virtual learning environment provided insight into tutors’ online practice. Goleman’s (2001) ‘Framework of Emotional Competences’ provided an initial structure but, after analysis, some competences were rejected and others were added. The paper suggests that a new group of competences are required that could support effective blended tutoring for mature learners as well as the recruitment and selection of tutors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".