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Section: Application Domains


  • Data-oriented Academic Counseling. Course selection and recommendation are important aspects of any academic counseling system. The Learning Analytics community has long supported these activities via automatic, data-based tools for recommendation and prediction. LACODAM, in collaboration with the Ecuadorian research center CTI (Centro de Tecnologías de Información, http://cti.espol.edu.ec/) has contributed to this body of research with the design of a tool that allows students to select multiple courses and predict their academic performance based on historical academic data. The tool resorts to interpretable machine learning techniques, and is intended to be used by the students before the counseling sessions to plan their upcoming semester at the Ecuadorian university ESPOL. The data visualization aspects of the tool, as well as the data science considerations and our emphasis on explainability are compiled in a paper that is under revision at the Learning Analytics and Knowledge Conference (LAK'20). The data science component of the tool was developed during the M1 internship of Mohammad Poul-Doust.