Applying a Conceptual Mini Game for Supporting Simple Mathematical Calculation Skills: Students’ Perceptions and Considerations
Bibliographic record
Abstract
Mathematics is an area of study that particularly lacks student enthusiasm. Nevertheless, with the help of educational games, any phobias concerning mathematics can be considerably decreased and mathematics can become more appealing. In this study, an educational game addressing mathematics was designed, developed and evaluated by a sample of 33 students of the fifth grade of a primary school. Each student played the educational game “Playing with Numbers” (PwN), performing additions with integers, additions with decimals and multiplications with integers for a total of one hour, divided into four sessions. Next, the sample was asked to provide feedback regarding specific questions, and the analysis of the results showed that the PwN application is attractive and delivers usage. The attraction of the PwN game probably owes its success to its competitive element, as users are driven to achieve high scores. The PwN application was also found to be easy to use, and this made the challenge of achieving a high score more appealing, as success depended only on the cognitive skills of the user and not on any weaknesses or difficulties raised by the application itself. The findings of this study show that students would benefit from educational games and would be happy to work within an environment that motivated them and indirectly forced them to deal with mathematical operations while playing.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".