Digital Environment, Disciplinary Climate, and Educational Inequality in Brazilian Schools: A Critical Evidence Synthesis and a Proposed Analytical Framework

Fábio Linhares

doi:10.67294/mndd3088

Authors

Fábio Linhares Federal University of Alagoas Author https://orcid.org/0009-0008-8648-1234

DOI:

https://doi.org/10.67294/mndd3088

Keywords:

School Digital Environment, Disciplinary Climate, Educational Inequality, Large-Scale Assessment, Analytical Framework

Abstract

The post-pandemic Brazilian school combines two apparently opposite technological problems: the exclusion that amplified learning losses while schools were closed, and the weakly regulated presence of personal devices that has since been associated with distraction and a less favourable disciplinary climate. This paper presents a critical synthesis of the evidence connecting the school digital environment, disciplinary climate, psychosocial safety and socioeconomic inequality in Brazil, drawing on official indicators from the 2018 and 2022 cycles of the Programme for International Student Assessment, on the 2024 National Survey of School Health, and on peer-reviewed studies. The paper does not estimate new psychometric models, and it states that scope explicitly: it is a structured review whose original contribution is an analytical instrument derived from the reviewed evidence. We propose the Digital Environment and Equity Profile, a four-layer framework that separates access, classroom attentional conditions, psychosocial safety and equity outcomes; maps each layer onto variables already available in official Brazilian and international databases; specifies an estimation protocol consistent with plausible values, replication weights and school-level socioeconomic composition; and defines screening rules that translate estimates into prioritisation decisions. We also document a measurement constraint that is frequently overlooked: in the 2022 cycle, the digital-distraction items belong to the same item set used to build the disciplinary climate index, which restricts how the two constructs may enter a single model. The framework is offered as a testable proposal, and its empirical validation is left to future work.

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