The Influence of Attendance, Communication, and Distractions on the Student Learning Experience using Blended Synchronous Learning
Bibliographic record
Abstract
A second-year engineering course at the authors’ institution was offered via a blended synchronous learning (BSL) method of delivery whereby students could choose to attend lectures live (face-to-face) or remotely (via a synchronous, live stream over the internet) during a summer semester. Survey and grade data were collected across two years of this offering. Attendance, interaction, communication with the instructor, and general distractions were main themes affecting the student learning experience both positively and negatively. Specifically, students found the remote access, the ability to ask questions, the teaching style, and having more time during the summer semester as positive aspects to their learning experience. Negative influences on their learning experience related primarily to their busy work schedules, technological issues associated with BSL, and typical summer distractions. Critically, our results indicate that attendance is a key indicator of student grades (after correcting for GPA), regardless of whether students attended lectures remotely or face-to-face: students attending more than 75% of the lectures performed on average 12% better than students who did not (p=0.0093). The consensus in the student comments was that the remote attendance option allowed students to attend in situations where the alternative was no attendance at all, implying that the potential gain in grades due to higher attendance may outweigh any potential impact the mode of attendance may have. Overall, a synchronous, remote attendance option may provide a lifeline to students who would not otherwise be able to attend a course, and (assuming a mode of interaction, such as the synchronous chat, is available) students do not perceive remote attendance as having a negative influence on their learning.
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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 | 0.003 | 0.022 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".