Student Perceptions of Remote Chemistry Lecture Delivery Methods
Bibliographic record
Abstract
Beginning in March 2020, instructors and students in educational institutions across the world have had to adapt to new virtual methods of teaching and learning. This study focused on obtaining the student perspective of remote chemistry lecture delivery methods across 13 Fall 2020 courses with students in varied majors of study and stages of degree completion. For students who experienced a mixture of asynchronous and synchronous content delivery within the same course, a majority of students preferred the asynchronous model. When all student participants were asked which content delivery model they would prefer, should remote learning continue, the majority of students indicated that a hybrid mixture of both asynchronous and synchronous opportunities would best support their learning. This was followed by a fully asynchronous model with fully synchronous being least preferred. While students in all years of study showed a preference for the hybrid model with even preferences for fully asynchronous and synchronous models, second-year students were more likely to select asynchronous learning over synchronous. For courses providing recorded synchronous content, the majority of students attended the live class, while a significant portion also made further use of the provided recordings, suggesting recorded content may be worth pursuing for future remote or in-person courses.
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 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".