Research on the E-Teacher in the K-12 Distance Education Classroom
Bibliographic record
Abstract
Compared to the post-secondary level, distance education at the elementary and secondary levels has received little attention from researchers (Kapitzke & Pendergast, 2005; Smith, Clark, & Blomeyer, 2005). This lack of attention is of concern given the rapid and broad growth of this form of education. In the United States, online education programs are experiencing rapid growth. For example, during the academic year 2005-2006, more than 90,000 middle and high school students were enrolled in state virtual schools in the Southern Regional Education Board, which represented a 100% increase in enrollments from the previous year (Southern Regional Education Board, 2006). While we might assume that research from contexts of post-secondary may inform K-12 distance education, Cavanaugh, Gillan, Kromrey, Hess, and Blomeyer (2004) caution against this assumption as follows: “The temptation may be to attempt to apply or adapt findings from studies of K-12 classroom learning or of adult distance learning, but K-12 distance education is fundamentally unique” (p. 4). The authors further observed that, although research in this area “is maturing” (p. 17), it has only been studied since about 1999. The current “explosion in virtual schools” (p. 6) creates a compelling rationale for continued efforts to conduct research on K-12 distance education.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".