If You Build It, Will They Come? Predictors of Teachers’ Participation in and Satisfaction with the Effective Classroom Interactions Online Courses
Bibliographic record
Abstract
The Effective Classroom Interactions (ECI) online courses were designed to provide an engaging, effective and scalable approach to enhancing early childhood teachers’ use of classroom practices that impact children’s school readiness. The created courses included several versions aimed at testing whether or not certain design aspects could increase participation and subsequent learning outcomes. The purpose of this study was to examine the extent to which early childhood teachers accessed the courses and varied in their a) participation in the core course content and b) optional discussion board as a result of the course experience they were assigned to as well as individual characteristics that may be associated with participation. Findings indicated that early childhood teachers accessed the course often on nights and weekends and reported high levels of satisfaction with their experience. Both persistence in the ECI courses and overall completion of activities were higher than those reported in other studies of online learning. Whether or not the participant was in the course that had regular interactions with the instructor, comfort with technology and took the course for credit consistently predicted participation, but not always in expected ways. Implications for exploring online learning as a feasible option for early childhood educators are discussed.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".