Mentoring Graduate Students Online: Strategies and Challenges
Bibliographic record
Abstract
The proliferation of online graduate programs, and more recently, higher education institutions’ moves to online interactions due to the COVID-19 crisis, have led to graduate student mentoring increasingly occurring online. Challenges, strategies, and outcomes associated with online mentoring of graduate students are of primary importance for the individuals within a mentoring dyad and for universities offering online or blended graduate education. The nature of mentoring interactions within an online format presents unique challenges and thus requires strategies specifically adapted to such interactions. There is a need to examine how mentoring relationships have been, and can best be, conducted when little to no face-to-face interaction occurs. This paper undertook a literature review of empirical studies from the last two decades on online master’s and doctoral student mentoring. The main themes were challenges, strategies and best practices, and factors that influence the online mentoring relationship. The findings emphasized the importance of fostering interpersonal aspects of the mentoring relationship, ensuring clarity of expectations and communications as well as competence with technologies, providing access to peer mentor groups or cohorts, and institutional support for online faculty mentors. Within these online mentoring relationships, the faculty member becomes the link to an otherwise absent yet critical experience of academia for the online student, making it imperative to create and foster an effective relationship based on identified strategies and best practices for online mentoring.
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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 | 0.027 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".