ABSTRACT
This chapter deepens the data-driven approach to designing trainee-centered learning processes and making educational activities more valuable and attractive. The proposed scenario harmonizes the field of analytics, shaping it into an educational domain, which deals with two complementary yet tightly intertwined contexts. In the former, academic analytics is leveraged better to understand learners' attitudes, skills, and knowledge weaknesses, while the latter focuses on identifying and supporting actual learners' needs. Within an adaptive learning framework, this work enhances the comprehension of teaching and learning within the educational domain by leveraging data gathered along the student learning life cycle. The integration of data mining and machine learning techniques makes this conceptual platform an adaptative and innovative tool able to bring out a relevant impact on the learning process in terms of the reinforcement and personalization of educational experiences.
