Modern Technologies and Applications of ICT in the Training Process of Teachers-Philologists
Bibliographic record
Abstract
The use of online technology of an experimental, gaming, competitive nature helps unite the virtual context and the acquisition of analytical skills in an environment, where personal communication is impossible, and the learning process requires high-quality preparation and understanding of the level of acquisition of speech skills. Studies show that within the conditions of distance learning of language and literature teachers (philologists), special difficulties are caused by the study of new technologies related to speech practice, stylistics, text creation; the reason for this is the insufficient number of group and heuristic activities, work in pairs, lack of sufficient speech practice of high communicative and intellectual level. Online quizzes as a technology of competition, games aims to increase the register, level and intensity of communication; they can potentially overcome the lack of authentic communication and academic traditions in the training of philologists. The purpose of this study is to show the prospects for the use of modern information and computer technologies. Intensive informatization of the educational process has its own organizational specifics: the willingness and availability of technical capabilities not only for students - philologists, but also for the teaching staff of universities and administrations. The condition for successful informatization is the ability of teachers to effectively use all available potential. The study focuses on the use and scope, difficulties in implementing information and communication technologies, and separate competitive interactive forms in higher education of language and literature teachers in terms of distance 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".