Use of ePortfolios in K-12 teacher hiring in North Carolina: Perspectives of school principals
Bibliographic record
Abstract
This study explored the perceptions of principals involved in the hiring process of K–12 teachers in 11 counties in southeastern North Carolina. Forty-nine principals responded to a survey on ePortfolio use in the hiring process: the pros and cons, desirable artifacts, stage of use, preferred delivery method, and improvements that can increase their usage. We examined each of these questions and whether certain factors (prior use, technology skills, and years as a hiring agent) predict principals' ePortfolio use. Our findings suggest that ePortfolios provide improved and current information about teacher candidates that is easily accessible and organized. Collectively, this allows principals to assess teacher candidates’ suitability for employment. Although, there are problems associated with ePortfolio use during hiring, which are detailed below, the results suggest that principals most frequently use ePortfolios during the interview process, prefer delivery via a website address, and that prior use is the best predictor of future ePortfolio use.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".