Impacts of a Redesigned Virtual Internship Program on Preservice Teachers' Skills and Attitudes.
Bibliographic record
Abstract
An important issue in teacher education is how to design and implement effective virtual internships for future educators. Today, these experiences should reflect best practices (as found in more traditional programs) by infusing constructivist values and strategies into the process. Interns can develop needed content knowledge and delivery skills through active learning in authentic, real-world environments. In this study, teacher specialists redesigned a virtual internship for senior physical education majors after evaluating a prior program considered inadequate (due to poor student outcomes and satisfaction levels). Using the literature, the specialists redesigned the experience to reflect constructivist assumptions during a summer orientation seminar and fall internship. Eight of 16 interns volunteered to participate in the research project to identify changes in their attitudes and expectations for the internship. During the summer seminar, interns served as curricular evaluation teams to review, analyze, and reconstruct the course they would be delivering to high school students. During the fall internship, pairs of interns met twice weekly with cooperating teachers. Using a web-conferencing program, they experienced authentic learning featured in these sessions. Results indicated that, after these experiences, interns felt more knowledgeable, capable, and enthusiastic about online teaching.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".