Organization of an Individual Approach to Teaching Mathematics to Non-Mathematical Pupils Under the Covid-19 Conditions
Bibliographic record
Abstract
The problem of teaching mathematics in modern Ukrainian schools is general, but it has become more difficult for students with a non-mathematical background. Despite numerous studies of this problem, no specific recommendations have been made. Therefore, to develop and implement an experimental method of teaching mathematics aimed at activating the cognitive activity of non-mathematical specialties pupils. The goal was solved by conducting a questionnaire among students and teachers, which allowed us to reveal and deepen the aspects of the specified problem. Two groups were created: experimental and control. The experimental group studied according to the new model of education, and the control group - according to traditional methods of teaching mathematics. The study revealed a complex of interrelated problems, both for teachers and students. Among the problems, the lack of motivational mechanisms and a complex pedagogical approach to explaining mathematics and the limited amount of teaching mathematics to students with a non-mathematical background are of primary importance. The results of the study indicate the need to introduce the specifics of conducting classes, which would focus on understanding the subject through imaginative thinking. The need to develop textbooks and manuals, which would focus on a more in-depth and understandable teaching of the subject with exercises and tasks for humanitarian areas, has been proven. At the same time, such measures became urgent due to the introduction of quarantine measures of the Covid-2019 pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".