An exploration of gendered differences in cognitive, motivational and emotional aspects of game‐based math learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background Digital game‐based learning (DGBL) has the potential to provide a gender inclusive learning environment for children. Objective The present study aimed to explore gendered differences among primary school learners in grades three, four, and five within the context of game‐based fraction learning from cognitive, motivational and emotional perspectives. Methods Two hundred and sixty‐nine participants completed a pre‐test and post‐test on fraction conceptual knowledge and surveys in math anxiety, intrinsic motivation and self‐efficacy. In addition, facial expression detection technology was employed to evaluate emotional states. Results In general, within the DGBL environment, boys and girls exhibited similar performance in both their understanding of fraction concepts and their motivational aspects. However, gender differences were identified and manifested uniquely across different grade levels. Specifically, third‐grade girls exhibited significantly lower self‐efficacy than boys, but after DGBL intervention, the gender gap in self‐efficacy was no longer significant. Additionally, third‐grade girls achieved significant improvements in both the competence and interest dimensions of their intrinsic motivation, while boys did not show significant improvements. In the fourth grade, girls exhibited a significantly higher frequency of angry expressions compared to boys during gameplay. Fifth‐grade girls' cognitive performance appeared to be less correlated with motivational factors compared to boys. Conclusions The results suggest that DGBL may help narrow the gender difference in math learning, with girls potentially benefiting more from DGBL than boys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it