Teacher Attitudes to Professional Development of Proficiency in the Classroom Application of Digital Technologies
Bibliographic record
Abstract
The paper deals with research focused on the opinions and attitudes of biology teachers on the application of digital technologies in the process of learning and teaching. The respondents were teachers, who participated in the national project called “Modernization of the Educational Process in Elementary and Secondary Schools” realized in Slovakia between 2008–2013. We briefly describe the course and the contents of individual modules, which were focused on the development and acquisition of specific skills in the field of effective use of modern educational technology. The key role in the methodical preparation of teachers was played by the 3rd module, which aimed to present the teachers with the examples of meaningful and methodically well prepared application of digital technologies in the teaching process, especially in connection with current digital educational contents and the curriculum of biology subject. The second part of the study includes analysis of satisfaction among the course participants with the content, level of expertise and difficulty level of the course, as well as the analysis of their opinions and attitudes on usability of created and available model methods in the real school practice. In conclusion, we present suggestions which could, facilitate improving the quality of biology teaching in schools, in order to reflect the real needs of society.
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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 | 0.003 | 0.015 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".