Time students spend reading threaded discussions in online graduate courses requiring asynchronous participation
Bibliographic record
Abstract
The authors report the results of a study that provides bases for comparison between the time necessary to participate in courses delivered asynchronously online and courses delivered in a traditional classroom setting. Weekly discussion threads from 21 sections of six courses offered as part of online, degree-granting, accredited, graduate programs were examined. The purpose of this research is to determine whether students are spending more or less time participating in an online course than in a traditional classroom. The discussion size (i.e., the number of words per discussion) was determined using the automatic word count function in MS Word. Once the word counts for each course section were determined, the average words per discussion were calculated. The authors used 180 words per minute to calculate the average reading time, based on the work of Ziefle (1998) and Carver (1985, 1990), in order to determine the average minutes per week a student spent reading the discussions. The study indicates that a typical, graduate-level, online, asynchronous discussion requires about one hour a week of reading time, and the time commitment for participatory activity is similar to that of traditional, face-to-face courses, given that it takes under two hours to compose initial messages and responses to the discussion prompt. Although these findings are informative, further research is recommended in the area of time spent on online course activities in terms of student hours earned to enable a direct focus on various student characteristics, such as English language competency and student level.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".