Designing gamified assistive apps: A novel approach to motivating and supporting students with learning disabilities
Bibliographic record
Abstract
The present research endeavor delves into the profound effects of gamified assistive applications on the levels of user engagement, motivation, and academic attainment within the population of students grappling with learning disabilities in the esteemed nation of Jordan. The study involved individuals who actively interacted with a gamified assistive application that was specifically developed to offer tailored educational opportunities and enhance their scholarly advancement. The analysis of descriptive statistics unveiled a noteworthy degree of app engagement, signifying the app's proficiency in captivating and maintaining students' focus. The utilization of paired-samples t-tests revealed noteworthy enhancements in both intrinsic and extrinsic motivation subsequent to the utilization of the application, thereby underscoring the favorable impact of gamified components on student motivation. Furthermore, a notable enhancement in scholastic attainment was noted, underscoring the application's influence on augmenting students' educational results. The findings of the correlational analysis unveiled a noteworthy association between the utilization of mobile applications, the presence of intrinsic motivation, and the attainment of academic success. This implies that heightened levels of engagement and motivation are linked to enhanced academic performance. The results of this study highlight the considerable promise of gamified assistive applications in fostering motivation and providing support to students who face challenges associated with learning disabilities. The incorporation of gamification into educational technologies presents educators with a promising strategy to cultivate active participation and elevate scholarly accomplishments within this demographic, ultimately advancing inclusivity and fostering educational triumph.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".