The good and bad of an online asynchronous general education course: Students’ perceptions
Bibliographic record
Abstract
The pandemic resulted in many courses being shifted to online delivery, but some courses are designed as online courses from their conception. Courses intentionally designed for online delivery should be well-received by students, but it is not clear which aspects of courses students find particularly appealing and unappealing. We examined students’ perceptions of one such online asynchronous course in psychology in order to better understand students’ preferences in terms of specific course elements. Students were asked to identify what they particularly liked and disliked about the course in two open-ended questions. Responses were then coded to quantify the frequency of each aspect of the course. An inductive and latent approach to coding was used, with codes being used to develop themes based on the underlying meaning of the text. Overall, students identified few negative aspects about the course. They particularly enjoyed the specific psychology content, format, and structure of the course, that it related to their real lives, and the flexibility provided by the asynchronous nature. The hope is that this information can be used to improve this particular course as well as inform instructor decision-making related to the design of online asynchronous courses in general.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".