Problems and Prospects of Formation of Digital Competence of Future Scientific and Pedagogical Workers of Higher Education Institutions Through Gamification: Opportunities Kahoot, Quizlet in the European Union
Bibliographic record
Abstract
This article discussed the trends and features of the formation of digital competence of future teachers through virtual games such as Kahoot and Quizlet. The purpose of this article was to identify modern features of the use of virtual games in the educational process of future teachers in European Union. To achieved this goal, an analytical study was conducted on the use of Kahoot and Quizlet in education. A logical, historical, and comparative legal method was used to research prospects of formation of digital competence of future scientific and pedagogical workers of higher education institutions through gamification. Using a comparative legal method, a comparative analysis of the level of use of gamification tools. As a result, conclusions were made about a significant increase in the level of use of gamification tools in various types of educational activities of future research and teaching staff. It was determined that there are negative and positive effects of gamification. It is concluded that the use of gamification tools in various types of educational activities requires students to develop additional skills that are necessary in today's world. Active use of Kahoot, Quizlet in education helps to improve students' adaptive and social skills, quickly analyze their achievements and easily convey new information. Moreover, this format contains features before the usual learning format. Therefore, the problem of developing appropriate methods and skills of gamification in education was becoming urgent. As a result, it was determined that the topic of effective use of gamification tools in education is becoming increasingly important.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".