Study of the adverse impact of learning barriers on the quality of education through the analysis of plitogenic statistics

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Pedro Fernando Mite Reyes
Carlos Fernando Morales Vera

Abstract

In the educational field, the study of learning barriers and their impact on the quality of education is a topic of growing interest, especially when approached through plitogenic statistics. This innovative approach unravels complex patterns that have traditionally gone unnoticed, revealing how various learning difficulties not only affect individual academic performance, but also have long-term repercussions on social cohesion and economic development. Plitogenic, with its ability to analyze multifactorial data, provides a deep and nuanced view of educational dynamics, highlighting the interactions between factors such as the socioeconomic environment, school infrastructure and educational policies. When considering the influence of these barriers, it becomes evident that education is not an isolated phenomenon, but rather an interconnected system where each obstacle can generate a domino effect, amplifying existing inequalities. Plitogenic statistics, by offering a holistic perspective, underscore the need for comprehensive approaches to address learning barriers. This implies not only direct interventions in the classroom, but also structural reforms that consider cultural and regional particularities. Ultimately, this analysis seeks not only to improve educational quality, but also to foster greater equity and social justice, ensuring that every student has the opportunity to reach their full potential.

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How to Cite
Study of the adverse impact of learning barriers on the quality of education through the analysis of plitogenic statistics. (2024). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 33, 16-27. https://fs.unm.edu/NCML2/index.php/112/article/view/546
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How to Cite

Study of the adverse impact of learning barriers on the quality of education through the analysis of plitogenic statistics. (2024). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 33, 16-27. https://fs.unm.edu/NCML2/index.php/112/article/view/546