An Exploratory Case Study of Online Instructors: Factors Associated with Instructor Engagement
Bibliographic record
Abstract
This study identified factors associated with instructor engagement in online courses. We believe that this topic deserves attention because quality of instruction is one of the strongest predictors of a successful online course. As researchers, our hope is that, by understanding the factors which influence perceptions of success by online instructors, policies and/or programs can be implemented to support online instructors, which, in turn, will result in higher quality online courses. This research was an exploratory case study in which the experiences of twelve online instructors were examined over one year. The identified themes based on the participants’ experiences will inform the direction of a larger quantitative study. Participant interviews were analyzed for evidence of positive and negative experiences and how frequently each occurred. We expected that participants who were less engaged in teaching would describe more negative experiences than other participants. Specific barriers to online engagement included lack of social presence, an increase in workload, and technological issues. Research priorities were determined to influence the instructors’ ability to cope with these barriers. Instructors who were hired to teach and conduct research held mixed and often negative feelings about teaching in online environments.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".