Bibliographic record
Abstract
The purpose of the study was to investigate the views of preservice teachers about use of slowmation in biology teaching. In the study, descriptive model was used. The participants were 12 third-grade preservice teachers in the department of Biology Education at Ziya Gökalp Education Faculty of Dicle University in the Spring Term of the academic year of 2016-2017. The research data were collected via observations and semi-structured interviews. In the research process slowmation activities were carried out with preservice biology teachers and at the end of these activities the preservice teachers’ views about the activities were determined. The data collected in the study were analyzed using the content analysis method. As revealed by the analysis of the data, the preservice teachers thought that the slowmation activities contributed to the development of some 21st century skills such as creativity, communication and cooperation skills, information literacy, research skills, technology and media literacy skills. In addition, the preservice teachers reported that the process of creating slowmation was interesting and that they entertained in the process. On the other hand, the preservice teachers also stated that it was difficult to create scenarios and that they experienced problems with the materials used. Moreover, it was seen that the preservice teachers faced problems with the video editing software and that they experienced disputes regarding the distribution of duties during group works. The preservice teachers also put forward suggestions regarding the use of slowmation activities in biology teaching saying that teachers and students could work together in the application process; that activities could be carried out to summarize the subject and to increase permanency; and that different tools and software programs could be used.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".