# Readability Formulas for Elementary School Texts in Mexican Spanish
## 5 Conclusions  
This study introduced two novel readability formulas, Linear-MX and GP-MX, designed to quantify the difficulty level of Mexican Spanish texts intended for elementary school readers. Linear-MX was developed using a linear regression approach, while GP-MX employed GP to derive concise, interpretable, closed-form expressions based on non-linear feature combinations. Both models were fitted using a representative corpus of 540 texts sourced from official Mexican public education textbooks and evaluated using a combination of statistical and regression-based performance metrics.  
The experimental analysis showed that the proposed formulas outperformed traditional Spanish and international readability formulas in predicting Mexican elementary grade levels. While Linear-MX and GP-MX perform similarly, GP-MX achieved the best balance between accuracy and interpretability, making it a practical tool for educational contexts where model transparency is essential. Statistical testing via ANOVA and [PERSON]'s HSD further confirmed that the proposed models more effectively captured significant differences across grade levels, especially where traditional formulas failed to maintain a consistent progression of complexity.  
Despite these positive results, the study presents several limitations. The models rely on surface-level linguistic features--such as sentence length, word length, and lexical patterns--which, while effective, may not capture deeper semantic or syntactic dimensions of text complexity. Additionally, the corpus, although representative of the national curriculum, excludes texts with indigenous vocabulary and may not fully reflect regional or stylistic variation across the broader Spanish-speaking population.  
Future work may extend this study in several directions. One avenue involves expanding the feature set to include indicators of syntactic complexity, lexical diversity, and semantic coherence, which could enhance both model interpretability and predictive power. Another promising direction is to adapt and validate the proposed models for other regional varieties of Spanish and for indigenous languages spoken in Mexico, thereby promoting greater inclusivity and generalizability. Additionally, systematically comparing datasets across languages and educational systems for readability modeling remains challenging due to structural and contextual differences, yet it offers a promising avenue for future research. Finally, incorporating qualitative assessments from educators and domain experts may help validate readability estimates and align them more closely with pedagogical standards.  
**Author Contributions**: Conceptualization, D.F.-D., M.A.A.-C., and M.G.S.-C.; methodology, D.F.-D., M.A.A.-C., M.G.S.-C., and A.Y.R.-G.; software, D.F.-D., L.R.-C., and M.G.S.-C.; validation, L.R.-C. and A.Y.R.-G.; writing--original draft preparation, D.F.-D. and M.G.S.-C.; supervision, L.R.-C. and A.Y.R.-G.; project administration, D.F.-D., M.A.A.-C., and M.G.S.-C. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research was funded by the Tecnologico Nacional de Mexico under grant number 23018.25-P.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: For reproducibility purposes, the set of Mexican Spanish texts is available at [[https://github.com/dfajardod/mexican_spanish_lectures](https://github.com/dfajardod/mexican_spanish_lectures)]([https://github.com/dfajardod/mexican_spanish_lectures](https://github.com/dfajardod/mexican_spanish_lectures)) (accessed on 7 June 2025).  
**Acknowledgments**: The authors are thankful to [PERSON] and [PERSON] for their valuable technical support in the Linear-MX analysis conducted in this study.  
**Conflicts of Interest**: The authors declare no conflicts of interest.