Time on Task: Perceived and measured time in online courses for students and faculty
Bibliographic record
Abstract
Objective : A cross sectional, online survey study examined active course time and activity for students and faculty in online courses compared to their perceptions of time. Methods : Student self-reports of their estimated course time and percentage of time on individual tasks, and faculty estimates of student time as well as their own course activity time were obtained. This was compared to actual individual and course summary activity data as recorded by the learning platform (Blackboard). Descriptive and t tests were analyzed using IBM SPSS Statistics 22. Results : Students and faculty generally agreed on the amount of time spent on task in all areas examined (discussion, assignments, tests and quizzes, messaging and communication, information searches, checking instructions and rubrics), but were significantly different for time spent off-line preparing Discussions and Assignments. Discussions, content materials, messaging and grade records were the most active areas. Students believed that the work in the online courses was more appropriate than did their instructors. There was no correlation between active course time and course grades. Conclusions : Students and faculty generally agreed in the amount of time spent actively in an online class, but grossly overestimated their time online. On line time did not correlate to course grade. The study adds to better understanding of the time sent in online courses.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".