Online Instructors’ Use of Scaffolding Strategies to Promote Interactions: A Scale Development Study
Bibliographic record
Abstract
A great deal of research has documented that interactions among students or between students and instructors are key to student success in an online learning setting. However, very little research has been statistically and systematically conducted to examine online instructors’ conscious and effortful use of scaffolding strategies to promote interactions in online courses. The purpose of this research was to develop a scale assessing online instructors’ use of scaffolding strategies to promote interactions. We employed a scale development method for the study. Exploratory factor analysis revealed one factor structure associated with instructors’ use of scaffolding strategies to promote interactions in online settings. Confirmatory factor analysis conducted with a different group of online students indicated that the one-factor model fits the data well. In addition, significant correlations with social presence and classroom learning community scales further demonstrated convergent validity. The new scale of online instructors’ use of scaffolding strategies to promote interactions demonstrated psychometrically sound validity and reliability.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".