UNDERGRADUATE STUDENT ENGAGEMENT WITH SYNCHRONOUS AND ASYNCHRONOUS COURSE ELEMENTS
Bibliographic record
Abstract
This study aims to understand student engagement with synchronous and asynchronous elements across the lecture and laboratory sections of a Civil Engineering undergraduate course. This course provided a unique chance to observe and compare synchronous andasynchronous elements as they run concurrently and in parallel. Behavioural engagement, a measure of how many students accessed a course element, was determined from logging data obtained from the course website. The experience of students taking part in virtual laboratoryexperiments was evaluated with surveys to monitor perception of the experiential laboratories transition to a virtual format. Examining the logging data of 95 students, it was found that students engaged with synchronous lecture material at higher rates and more consistently throughout the semester, averaging 69% participation, whereas asynchronous lectures averaged 33% participation by the suggested date of viewing. Notably 60% of studentsaccessed the supplementary asynchronous concrete lab video within the recommended timeframe, suggesting when provided as a supplementary resource and in a creative format, there may be higher levels of engagement. This study shows that asynchronous content, despite being valuable for self-paced learning and accessibility, should not be the primary form of student engagement as students accessed it at a less consistent and routine pace.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".