Managing e-learning metadata within ToolboX.Academy: knowledge acquisition with Big Data algorithms at the student and group levels

E-learning is not only providing new means to access knowledge, and changing the way teaching takes place at the school and at home, it is also paving the way to learn more and know better about the students' skills, competences, attitude or disorders. Statistics and AI-based big data are the methods to acquire this knowledge, that is of great value for educators and managing teams. Indeed, what looks like a simple interaction between the student and yet another application interface, actually generates a considerable amount of metadata which holds the clues of how the student's cognitive processes deal with the task in question. This metadata, when treated properly, reveals useful knowledge to improve education, as a recent experience with the ToolboX Academy environment has shown. While atypical slopes in learning curves might point toward ADHD or intellectual giftedness, recurrent typing errors might reveal dyslexia, or discalculia might be detected by numeric errors over the average. What is more, tasks in this environment are specially designed for detecting other disorders, like daltonism. These are but examples of what coding environments can reveal with basic big data analysis, but the fine-grain interaction does actually store even more data, and group-level processing can also disclose deficiencies in the teaching process, in general or particular subjects, mainly in the STEM area. In this paper, we describe the approach followed by ToolboX Academy to disclose this information.