Assessing Data Landscapes for Quality Education in Latin America: A FAIRness Perspective on Brazil, Colombia, and Peru<\/a><\/h4> Abstract: <\/span> Despite the increasing availability of data used to inform educational policies and practices, concerns persist regarding its quality and accessibility. This study surveys quality education data from Brazil, Colombia, and Peru and evaluates their alignment with the FAIR principles and availability to support academic analytics (AA) and learning analytics (LA). We identified and analyzed 112 data sources, from which 93% of the data sets originate from government repositories and open data platforms, with coverage of macro-level data relevant for AA but lack of granularity for LA. The FAIR assessment showed 50% of compliance with findability (F), 33% for accessibility (A), and less than 50% for both interoperability (I) and reusability (R), which limits broader utility. Moreover, these diverse data sources present limitations in quality assurance metrics such as\u201cinstitutional development\u201d and\u201cquality management.\u201d We conclude by offering recommendations, emphasizing the need for enhanced data frameworks that bridge macro-and micro-level data for AA and LA to enable data-driven decisions for improving educational quality in Latin America. <\/p>\n<\/span><\/p><\/div><\/article><\/section><\/div><\/div><\/div><\/div><\/div>
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