Office Hours as Teachable Moments: Structuring Student-Instructor Interactions Outside the Classroom
Bibliographic record
Abstract
Students and instructors often approach office hours with an equal lack of enthusiasm. For students, office hours can be inaccessible due to scheduling conflicts or feelings of intimidation when approaching their instructor. For instructors, many report that students do not show up and when they do the visits are brief and linked to assessments. Despite this ambivalence, this workshop argues that office hours are an untapped and significant pedagogical tool to support student-faculty interactions and improve student outcomes.\nSubstantial research highlights the benefits of student-faculty interaction outside the classroom. In the literature, office hours are the predominant form of communication and informal faculty-student interactions have been shown to support students’ academic performance and personal growth. Researchers have found that the frequency of informal interactions that focused on academic topics had a significant positive influence on students. Studies also note that structured interactions have a greater impact than those that were more casual in nature. Additional research has highlighted that it is not necessarily the frequency but the quality of the interactions that supports positive student outcomes.\nThe purpose of this workshop is to provide participants with strategies to increase opportunities for teaching outside the classroom, and specifically during office hours.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".