Large Online Courses: A Constraint on Instructor Presence and Higher-Level Thinking
Bibliographic record
Abstract
A growing body of literature claims instructor presence is crucial in an online course learning environment. In this paper, the authors contribute to this literature with empirical research related to instructor presence and how class size influences it. This study investigated whether class size was predictive of students’ ratings of instructor presence. The findings of this study suggest that in courses with higher-level skills as learning objectives, as the class size increased, students rated instructor presence lower. This result affirms existing research that explains courses that encompass constructivist skills or higher-level thinking benefit from the community of inquiry model. This model makes clear that instructor presence is imperative for effective student learning, and the implication of this directive is that class size needs to be adjusted (in many cases, it needs to be lowered) to provide a teaching and learning environment adequate for strong instructor presence. The ripple effect of this yields positive student satisfaction and student success online.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".