Which Technologies Do Pre-Service Teachers Prefer to Use While Presenting Their Teaching Skills and for What Purposes Do They Use These Technologies?
Bibliographic record
Abstract
This research aims to determine the technologies that pre-service science teachers prefer to use in micro teaching presentations performed for improving their teaching skills and to determine the purposes of using these technologies. For this purpose, the case study model was used in the research. The research was made with some 48 pre-service science teachers. In the research, data was collected from the presentation files the pre-service science teachers had prepared with respect to the microteaching applications, from the instructor’s observation notes on their presentations, from the view form and from the semi-structured interviews. According to the findings obtained from the research, the pre-service science teachers used computers, projection apparatuses, overhead projectors, videos, animations, simulations and microscopes the most in the microteaching applications. The pre-service science teacher’s expressed that they used technology primarily for reasons such as enhancing the comprehensibility of the subject, concretizing abstract subjects, ensuring visuality and saving time. Considering these results, some recommendations were made regarding the use of technology in science courses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".