Instructors’ Perceptions of Instructor Presence in Online Learning Environments
Bibliographic record
Abstract
<p class="2">As online learning continues to grow significantly, various efforts have been explored and implemented in order to improve the instructional experiences of students. Specifically, research indicates that how an instructor establishes his or her presence in an online environment can have important implications for the students’ overall learning experience. While instructor presence appears to be an important aspect of online learning, more research is needed to fully understand this construct. The purpose of this study was to consider online instructors’ perceptions related to presence, beliefs about actions, and the perceived impact of instructional presence. Using an explanatory multiple-case study approach, this research considered the perspectives of 13 instructors teaching in an online master’s program at a large Midwestern public university. Results indicate instructors viewed instructor presence as an important component in online courses but their reasons varied. Furthermore, the instructors discussed a number of communication strategies they used, the importance of using such strategies to connect to students, and the potential impact of these strategies on student participation and learning. Additional themes from the interview data are discussed, and implications for online teaching and learning are suggested.</p>
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".