Digital Technologies for Introducing Gamification into the Education System in the Context of the Development of Industry 4.0
Bibliographic record
Abstract
The main purpose of the article is to model the stages of using digital technologies for introducing gamification into the education system. In recent years, gamification has been constantly on the list of trends in Industry 4.0. It is being researched by specialists in academic and corporate training, as well as by individual educational institutions. Therefore, we believe that we should take a closer look at this technology. The methodology implies the use of information-graphic modelling methods. Based on the results of the analysis, a multi-stage model of the use of digital technologies for the introduction of the gamification system into the educational process for a specific socio-economic system was formed. The study has limitations and they relate to the use of one educational institution and do not take into account all the digital technologies that can be applied in accordance with the research topic. Further research requires the question of analyzing the complexity of the gamification implementation system in modern conditions and determining what negative consequences it can bring to the socio-economic system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".