Development of professional qualifications of teachers based on the application of an online course
Bibliographic record
Abstract
The education sector is currently developing dynamically. As of 2015, there are a huge number of online platforms in online education for remote communication and training in all fields. On these platforms, students can receive training and feedback through online broadcasts based on educational content, and they can supplement their knowledge through the completion of various tasks. In Kazakhstan, such platforms in the Kazakh language are few. These projects are being developed and implemented in developed countries of the world, such as Europe, the USA, Canada, Russia, etc. Now, the educational platforms in these countries are being used reliably and at a high level. Unfortunately, in the field of education, such platforms and digital technologies are not used in their own tenge, so a number of works (seminars, courses, trainings, etc.) should be carried out in order to improve the digital literacy of teachers. In this regard, the article defines the content of the educational process in a new way, corresponding to the development of teacher competence in the field of education, and the need for it to be based on educational technology. Educational technologies aim to improve the capabilities of digital technology in the educational process and enhance the digital knowledge of teachers through the creation and use of massive open online courses. The article defines the direction of changes in the field of education. The emergence and development of methods of its management correspond to the development of the teacher's skills in the field of education. The essence of the educational system is revealed as the basis for the formation of professional skills of teachers in the learning process. It reveals the nature, structure, functions, and prerequisites for organizing the process of improving the competencies of future teachers. These processes are based on the content of technical support, including educational technology. The relevance of the problem we are studying is obvious, given the fact that the President of the country, Kassym-Jomart Tokayev, has stated that for digital transformation to be successful, every employee must have the necessary amount of digital skills. The research methodology was based on the literature on philosophy, education, psychology, and methodology, as well as research and analysis of legal texts.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".