DATA SCIENCE IN BASIC EDUCATION: AN EVIDENCE-BASED FRAMEWORK INTEGRATING LEARNING AND ASSESSMENT

In an increasingly data-driven society, preparing new generations with robust competencies in science, technology, engineering, arts and mathematics (STEAM) is essential. Within this context, Data Science (DS) education requires the development of foundational mathematical concepts, terminology, and cognitive structures that support students’ progression from introductory to advanced levels. learnipedagogical approaches must enable learners not only to understand data but also to creatively and efficiently apply knowledge to tasks such as data collection, preprocessing, analysis, and visualization. This paper identifies persistent gaps in the design and evaluation of DS teaching initiatives in Basic Education and proposes an evidence-based pedagogical framework to address these challenges. Grounded in international literature and cognitive psychology, the framework integrates well-established learning principles, such as distributed practice, retrieval testing, and iterative project-based learning, with formative assessment tools that strengthen retention, transfer, metacognition, and engagement. The analysis demonstrates that the lack of coherent assessment models weakens the institutionalization of DS as a curricular component and limits its pedagogical effectiveness. The proposed framework offers a unified structure that aligns teaching strategies, learning progressions, and assessment instruments, while remaining adaptable to diverse lol contexts. By centering instructional design on scientific evidence, the model supports teachers in implementing practices that are both pedagogically sound and operationally feasible. The study concludes that fostering data literacy in Basic Education through evidence-based teaching is a crucial step toward promoting educational equity, digital citizenship, and students’ capacity to navigate and interpret the information-rich environments of the current digital era.