Uma ferramenta para recomendação pedagógica em mineração de dados educacionais

Abstract

This work is about the creation of a tool for pedagogical recommendation which objective is to provide teachers, from web-based courses, personalized pedagogical recommendations generated based on the mining results of their students' educational data. In order to guide this creation, we propose the Pedagogical Recommendation Process that counts on the coordinated work and cooperation of the Human Intelligence (domain specialists) and the Artificial Intelligence (computational tools). The process is constituted of four steps that occur in a sequential and cyclic way, starting with "Detect Practices", where we detect if there are actions affecting the teaching and learning process. Is the next step, "Discover Patterns", we use educational data mining techniques, based on predefined mining scenarios, to find patterns with pedagogical significance for the practices detected. In the following step, "Recommend", it is where appropriate recommendations are offered, given the students' current pedagogical situation. Finally, the "Monitor and Evaluate" step, where it is analyzed whether the students were positively affected by the recommendations and if they were relevant. The proposed tool was used in a case study with real data provided by a Spanish language course with 200 students enrolled, who produced more than 700 megabytes of information contained in, approximately, 1220000 triples. As results we were able to detected practices and the patterns associated to them, which were used to create recommendations, evaluated (relevance) by specialists in the educational/pedagogical domain and made available for the final users (teachers) to suggest them to their students.