Data science in education: interdisciplinary contributions
Keywords:Computer Science, Education, Data Science, Machine Learning, Interdisciplinarity
The present paper aims to identify the contributions of Information Science (IS) and Computer Science (CS) to meet the informational needs of the educational context. The methodology used is exploratory, descriptive, and applied. A qualitative approach is used to process data retrieved through bibliographic research on data science applied to education. The study brought together the theoretical and practical contributions of IS and CS techniques capable of addressing the main informational challenges of educational environments reported in the literature. The preliminary results obtained consist of connections between concepts and techniques from the two knowledge areas that propose solutions for informational needs in education. Data mining, fuzzy logic, natural computing, text mining, data warehouse, ontologies, semantic web, and information retrieval were the most evident contributions identified when analyzing the demands of students, teachers, and managers. The conclusion reached is that articulating interdisciplinary knowledge of CS and IS in proposing data-driven approaches in the educational context is possible and necessary. These contributions proved capable of dealing with the challenge of supporting education in different ways by enabling even more robust theoretical and methodological solutions.
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