Tutoring during the pandemic: mentoring tutors’ formative experiences using digital and digital multimodal texts
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
This survey-design study examined how 228 middle school preservice teachers perceived the implementation of digital and digital multimodal texts during course-required, mentored, tutoring sessions delivered in face-to-face and online settings prior to, during and toward the end of the COVID-19 pandemic. Tutors were able to recognize that texts could be used to elicit affective responses from their students, and had the potential to differentiate their lessons in accordance with learners’ needs, but the technology challenges they faced seemed insurmountable to some. Given their lack of teaching experience, tutors struggled to determine the appropriateness of the resources and they held distinct perceptions of the accomplishments and challenges related to their tutoring sessions. Mentor responsiveness exhibited by honouring tutors’ adaptive expertise can be seen as an important aspect of fostering tutors’ confidence. Focusing on the role of the mentor in preservice teachers’ tutoring field placements is a suggested area for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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