La Alfabetización Cuantitativa en estudiantes de Tercer Grado de Primaria a través de un Juego Serio
Bibliographic record
Abstract
The last results of national and international evaluations, show that learning math is complex for Mexican students. Different plans and techniques have been implemented to counter this problem, one of them is the use of different technology in the classroom. During the last two decades, the videogame industry in Mexico has gained great traction among children, teenagers and young people, which is why the advantages of these kind of technologic tools must be harnessed. In this paper, we present a serious game to improve quantitative literacy in children studying the third grade of primary school. To design it, an iterative design model that contemplates four stages was use: planification, development, evaluation and improvement; emphasizing the instruction design. Through a quasi-experiment during a two-month period, the game was tested in a class of 33 morning shift third-grade students. The results obtained demonstrated quantitatively an increase in the students’ skills. It was shown, that out of the three subconstructs that constitute quantitative literacy, two of those (natural numbers and mathematical operations) showed significant improvement after treatment. The students enjoyed and engaged with the serious game, which is why it is expected to use this tool in the future in different Mexican communities.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".