Student perspectives of online teaching: Lessons learned for the post-COVID classroom
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 instructors return to in-person teaching and learning following online teaching during the COVID-19 pandemic, we can build from the experiences gained and incorporate various online resources into our campus-based classes. Drawing from student evaluations of teaching, a post-course student survey and learning management system (LMS) analytics, we documented students’ perspectives of online teaching and learning in a large introductory science course offered as a flipped classroom, and reflect on student and instructor perspectives as we return to campus-based teaching and learning. Results suggest that what students liked and what they perceived as effective often did not align, and that instructors need to consider good pedagogical practice when evaluating student comments. We identified strategies that we can carry forward to enhance our large introductory science course including a weekly course structure, synchronous classes and laboratories supported by asynchronous content, and taking advantage of recent advancements in online teaching and learning tools for discussion forums, practice exams and assessment.
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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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