Digital technologies have transformed how information is accessed and shared; however, individuals with disabilities continue to face significant barriers in engaging with educational content. The shift to electronic formats presents an opportunity to promote inclusion by integrating accessibility features. Accessible e-books are essential for fostering equitable participation in learning environments, supporting lifelong learning, and aligning with global sustainability goals such as SDG 4 (Quality Education) and SDG 10 (Reduced Inequalities). While international frameworks like the Web Content Accessibility Guidelines (WCAG) advocate for accessible publishing, significant gaps remain. This paper presents a Systematic Literature Review (SLR) aimed at examining the role of accessibility features in shaping the quality, usability, and affordability of e-books. Based on well-defined guidelines, we defined and executed an SLR protocol, identifying 1618 studies in seven different scientific databases. At the end of the review process, we selected 74 studies to answer four research questions related to the accessibility features employed by e-books and their reading platforms. By synthesizing existing evidence, this review highlights barriers, best practices, and strategic pathways to inform educators, publishers, and policymakers. Ultimately, this study advocates for the development of inclusive and sustainable digital content ecosystems that meet the diverse needs of all learners.

Forecasting enrollments for policies such as the Brazilian National Textbook Program (PNLD) is hindered by traditional global predictive models that ignore school heterogeneity and often produce algorithmic invisibility in vulnerable contexts. The study proposes a Context-Aware Forecasting Framework based on split modeling strategies, using Direct Bipartite Graphs and Clustering Techniques. Experimental results show that context-specific feature engineering enables drastic dimensionality reduction—compressing 20 features within "Cluster 1" into a single PCA signal and reducing "Cluster 2" to 20 relevant attributes, representing a 94% dimensionality reduction in 81.25% of the tuples — while maintaining comparable predictive performance.

O Projeto de Lei Complementar Nº 235/2019 que institui o Sistema Nacional de Educação (SNE) é considerado o princípio da colaboração entre os entes da federação na adoção de políticas educacionais. Neste artigo é proposto um modelo conceitual do SNE na concepção de Sistemas de Sistemas com foco na Câmara de Apoio Normativo (CAN) e com o propósito de estabelecer uma visão fundamental para compreender e nortear a aplicação prática e sistematizada do que ainda não foi estabelecido em lei, ou seja, a gestão dos atos normativos no Brasil, iniciando uma ampla discussão nacional sobre o tema. Para a construção deste modelo, foram elaborados artefatos de UML, que permitem visualizar a estrutura e as relações entre os diferentes componentes do SNE, facilitando a análise e o entendimento do sistema proposto.