Bibliographic record
Abstract
This study aimed to examine the preservice teachers’ views on the process after entering Code.org and block-based programming (Scratch) training programs, which are carried out by the peer learning method. The study group of the research consists of 41 preservice teachers at the Computer Education and Instructional Technologies departments of a state university and took the Special Teaching Methods 2 course in the spring semester of the 2017-2018 academic year. Considering the criteria determined by the researcher, 7 preservice teachers were selected as educators. As students, 34 preservice teachers participated in the study. In this study, a qualitative research method was used to determine the opinions of preservice teachers on the Code.org and Scratch training programs, which are carried out by the peer learning method. As a data collection tool, the opinion form for the Code.org training programs, the structure of opinion determination on the Scratch training programs, and the personal information form were used. The total duration of the study consists of eight weeks. Data from the preservice teachers were collected weekly using data collection tools and a content analysis technique used in the analysis of the data. At the end of the study, the opinions of the preservice teachers on the study conducted with the peer learning method were determined. It can be said that preservice teacher generally has positive views on peer learning and are satisfied with the peer learning method.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".