The Effectiveness of Gamification Elements for the Development of Future Culturologists’ Digital Competence
Bibliographic record
Abstract
The aim of the research was to experimentally prove the effectiveness of gamification of learning for the development of future culturologists’ digital competence. The research was based on the information from the monitoring of the subject or professional module, as well as a package of control and measuring materials. It also involved diagnostics of activity component, diagnostics of the level of digital component. Statistical tests — the Anderson-Darling test, the Cramér–von Mises test, Kolmogorov-Smirnov test, the Shapiro-Francia test, the Wilcoxon test, Mann-Whitney U-test, Student’s t-test — were performed to formally verify the consistency of data with the normal distribution law. The greatest differentiation was observed for cognitive, praxiological and reflective criteria. The increase in the cognitive component was promoted by the implementation of strategies for adapting educational content and multiple control of the students’ self-education process. The significant increase in the praxiological component was determined by the intensified student learning activity and, accordingly, the development of the activity component. In all tests, p significantly exceeds the fixed level of significance, so we can conclude that the data of the control and experimental groups were distributed uniformly at the beginning of the experiment. By testing the hypotheses H0 and H1 for EG and CG at the end of the pedagogical experiment, it was found that the statistics for the t-test exceeds the critical value and is in the range of significance. So, the approbation of the research results revealed significant differences in the learning outcomes of CG and EG, which allows us to state the effectiveness of gamification of learning. Based on the research results, we can argue that the gamification of learning has a significant impact on the development of future culturologists’ digital competencies. The study showed a significant positive impact of the introduction of gamification elements in the learning process. Further research should focus on studying ways to improve future culturologists’ digital competencies. It is necessary to study the impact of Internet technologies on the future culturologists’ professional development.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".