Investigating the Concerns of First-Time Distance Education Instructors
Bibliographic record
Abstract
The purpose of the study is to investigate the concern developments of first-time distance education instructors using the concerns-based adoption model (CBAM). This study used stages of concern (SoC), a component of CBAM, as its theoretical framework. A descriptive case study was implemented, which focused on the adaptation processes of nine instructors lecturing for the first time via distance education. The instructors attended a two-day training, which was designed based on their initial concerns. Then instructors implemented their courses for four weeks via distance education. While the informational and personal stages (self-concerns) decreased compared to the initial findings, the consequence stage increased in intensity. However, self-concerns remained predominant in the process despite the reduction in self-concerns and increase in the consequence stage. Based on the findings, the implications for distance education and recommendations for addressing the instructors’ concerns are discussed. Recommendations for alleviating the concerns of first-time distance education instructors include: the provision of ongoing concern-based interventions that incorporate technological, pedagogical, and content knowledge; providing working examples related to distance education from which instructors can learn vicariously; and encouraging collaboration among instructors.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".