Student Profile Eronomically Adapted to e-Learning. A Data Clustering and Statistical Analysis based Survey

摘要

In the present work we carried out a study over a 4 years period in order to develop a student profile that matches computer-assisted learning. In our opinion, much of the teaching-learning effort will be reduced if the forms of education that fit each individual can be correctly identified. The ergonomics of teaching / learning comprises the correct identification of the student profile so as to connect with the right method and tools for learning. In the process of student profile identification we used the statistic analysis, association rules, and the data mining clustering techniques based on the K-means algorithm.