Instructor Presence and Student Satisfaction Across Modalities: Survey Data on Student Preferences in Online and On-Campus Courses
Bibliographic record
Abstract
Post-COVID-19, many, if not most, college and university instructors teach both online and face-to-face, and, given that online courses historically have higher attrition rates, designing and facilitating effective online courses is key to student retention. Students need online and on-campus courses that are well designed and facilitated, but even well-designed classes can be ineffective if students feel lost in the course or disengaged from the instructor. We surveyed 2,007 undergraduate students at a public, metropolitan university in the United States about the best and worst classes they had taken at the university. The resulting data revealed important consistencies across modalities—such as the importance of clear instructions and instructor availability. However, students responded that instructors matter more in face-to-face courses, where they can establish personal relationships with students, whereas assignments “stand in” for instructors in online classes. These findings support the need for increased faculty professional development in online course design and facilitation focused on student experience as well as faculty expertise.
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.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".