A Model of Future Mathematics Teachers' Preparedness to Organize Mobile Learning for Schoolchildren
Bibliographic record
Abstract
Born in the digital world, children cannot imagine life without mobile devices and technologies, which contributes to the transformation of the education system. Mobile devices allow getting information on the Internet anywhere and at any time, the methodology of teaching subjects changes accordingly, the educational process becomes interactive. Mobile technologies and devices have an effective didactic and methodological potential, which requires targeted training of future teachers for their use in teaching activities. The purpose of the paper is to theoretically substantiate and develop a model for forming the readiness of a future mathematics teacher to organise mobile learning for schoolchildren. To create a model of future teachers' readiness for mobile education of schoolchildren, system-activity, personality-oriented and analytical approaches were applied. Analysis and generalisation of the research results of domestic and foreign scientists on this problem are the main research methods, as well as conceptual and terminological analysis and pedagogical modelling. The paper substantiates the relevance and necessity of purposeful preparation of future mathematics teachers to organise mobile education of schoolchildren. The concept of mobile learning of schoolchildren is clarified, its main features are determined. The readiness of future mathematics teachers to organise mobile learning of schoolchildren is considered as a set of motivational, personal, theoretical, activity and reflexive components, which allowed us to systematise and model the process of training teachers for this activity. The model presented by the author, the main components of which are conceptual, content, activity and reflexive blocks, reflects the peculiarities of a future mathematics teacher's readiness for mobile learning of schoolchildren. The developed model will allow us to reach a higher level of training of mathematics teachers, providing personal and professional development of students.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".