The attitude of teachers to the opportunities and risks of spiritual and moral education in the context of digitalization
Bibliographic record
Abstract
The relevance of the research is due to the fact that the digitalization of the world educational system as a modern trend in the development of education requires serious reflection and solutions to a number of problems. The use of digital educational content, in addition to its obvious advantages, also contains a number of very likely risks. Including risks related to the spiritual and moral sphere of the individual. The purpose of our research is to study the attitude of teachers to the opportunities and risks of spiritual and moral education in the context of digitalization, as well as to find ways to minimize possible risks. The study involved teachers of the Chelyabinsk region who had advanced training in teaching the discipline "Fundamentals of religious cultures and secular ethics" (on the basis of the Institute of continuing and additional education of the South Ural state humanitarian and pedagogical University). The survey method was used in the study. As a result of the survey, it was revealed that the main effect of using digital technologies, according to teachers, is better memorization of visually perceived information (72% of students), a third of respondents note the possibility of taking into account the individual characteristics of students, and a quarter of respondents – an increase in creative potential. Achievement of subject-based learning outcomes is, in the opinion of teachers, the main effect of using digital content, the educational effect of digitalization is not noted by respondents. More than 80% of teachers see the risk of using digital technologies in reducing the time for live communication, more than 60% – in reducing the communicative activity of children, in reducing interest in printed sources of information. The conclusions are related to the need for further research of this problem in the following areas: analysis of educational opportunities of existing digital educational content of spiritual and moral orientation and determination of criteria for evaluating such content; selection and creation of effective methods of using digital technologies that ensure the formation of students ' experience of emotional and value attitude to spiritual, historical and cultural heritage, as well as the ability to apply the knowledge gained in various life situations; training of teachers who are competent in the field of spiritual and moral education.
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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 | 0.009 |
| 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) | 0.003 | 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".