Strategies Used by Educational Technology Faculty to Mentor Online Doctoral Students in Research Experiences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As online doctoral education continues to grow, faculty members are faced with mentoring an increasing number of students. To effectively support these students’ research projects, faculty mentors need to develop strategies that take into account the unique challenges of online environments. This study, based on Crawford et al. (2014) theoretical framework for online graduate mentoring, aimed to identify the strategies used by faculty members who advise online students in Educational Technology doctoral programs during research experiences. The data was collected through a survey completed by 24 mentors, and five of them also participated in individual interviews. These mentors were faculty members in online educational technology programs in the USA and Canada. The study found that faculty mentors used strategies associated with both the academic and psychological domains of Crawford et al.‘s framework. The practical implications of these findings are discussed in relation to enhancing the quality of mentoring in online doctoral education.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it