Teaching Presence, Faculty Characteristics, and Student Perceptions of Teaching Effectiveness: A Study of Online Teaching
Bibliographic record
Abstract
Using the Community of Inquiry framework (CoI) to ground the study, this research study explored the relationship between teaching presence behaviors, select faculty characteristics, and student rating of instruction for teaching effectiveness. Data from the Individual Development and Educational Assessment (IDEA), conducted at Ottawa University during the 2021-2022 academic year on 194 faculty members and 609 course sections, were analyzed to determine how faculty members at Ottawa University spend their time engaging in teaching presence behaviors within the Learning Management System (LMS) of their eight-week online courses.Results revealed that faculty members are spending their time differently when engaging in canned online courses. Furthermore, this study suggests that male faculty members, part-time faculty members, and faculty members with master’s degrees had higher levels of engagement in certain teaching presence variables, and that the number of years teaching were positively correlated with the number of course logins, course actions, total discussion board posts, and the character count of course announcements. Additionally, results from the study indicated that certain teaching presence variables were positively correlated with student rating of instruction. Finally, results from this study indicate that the number of years teaching, the total number of course announcements, and the character count of course announcements were significant predictors of student rating of instruction for teaching effectiveness.Implications of this study include consideration of how university leadership structure pay to include rewarding long-standing faculty members, and in training and development for faculty members regarding increasing specific teaching presence variables to create a social community of online learning that supports student perception of teaching effectiveness.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".