Should Instructors Require Discussion in Online Courses? Effects of Online Discussion on Community of Inquiry, Learner Time, Satisfaction, and Achievement
Bibliographic record
Abstract
Online discussion is a commonly used means to promote student understanding of a topic and to facilitate social interaction among students or between students and instructor; however, its effects on student learning in online learning environments have rarely been investigated. The purpose of this study was to examine the role of online discussion in student learning experiences measured with community of inquiry, learner time, satisfaction, and achievement. One instructor taught the same online course for three consecutive semesters using three different conditions. During one semester enrolled students engaged in no discussion, during another semester they engaged in discussion without instructor participation, and in the remaining semester they engaged in discussion with active instructor participation. No significant differences were found among conditions in cognitive presence and the instructor’s teaching presence, whereas significant difference was found in social presence among conditions. No significant differences among conditions were found time spent on Blackboard, course satisfaction, and student achievement. Implications for online teaching and learning as well as for designing an online course conclude the paper.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".