Evaluate the Attıtudes of the Pre-Servıce Teachers towards STEM and STEM’s Sub Dımensıons
Bibliographic record
Abstract
As the importance of interdisciplinary studies is on the rise, countries have developed innovative educational approaches. One of these innovative approaches is STEM. STEM focuses on interdisciplinary cooperation, systematic thinking, openness to communication, ethical values, research, production, creativity and problems, focusing on the intersection of knowledge and skills in science, technology, mathematics and engineering, it is a new learning teaching approach that aims to gain the ability to solve. In this context, the aim of this study is to evaluate the attitudes of the pre-service teachers towards STEM and STEM’s sub dimensions. Therefore; it was used as a data collection tool from attitude scale for Stem and Stem's sub dimensions developed by Keles, Kiremit and Aktamis (2017). In the data collection tool, 5 scales were developed in order to reveal the existence of the relationship between pre-service teachers' attitudes towards STEM and their attitudes towards; science, technology, mathematics and engineering. The related scale was applied to 204 pre-service teachers in various departments of the Faculty of Education at Sakarya University. In the research, the necessary correlations were made by considering the demographic characteristics such as gender, age and class of teacher candidates. The overall average and standard deviation values are taken into account, when explaining the data differences for the sub-dimensions of the scale. According to the results obtained from the relevant data collection tool, when pre-service teachers' attitudes towards STEM and STEM sub-dimensions were evaluated; especially in the Mathematics and Engineering dimensions, it was revealed that their attitudes were more positive and they were indecisive in other dimensions (Science and Technology). As a result, it is thought that the acceptance of STEM method by teacher candidate, which is an innovative educational approach of the research results, will contribute to the literature and the future studies in this field.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".