Adaptive Learning Content Recommendation using a Probabilistic Cluster Algorithm

Nowadays there are many research using the LDA (Latent Dirichlet Allocation) algorithm to find preferences and characteristics for recommendation systems. In some of the most relevant studies, the recommendation is based on the student's level of evolution within the discipline. This work presents a new recommendation approach with the LDA algorithm. The approach differs from previous LDA studies since the recommendation technique is based on the experiences and preferences from a group of students and not just an individual student. The main objective is to verify, through simulation, whether the methods used, and the algorithm can generate recommendations close to those considered ideal. The obtained results indicate that the application of the LDA for creating groups to generate recommendations provides a good result in delivering content and practices in accordance with the student's interests. It's empirical research, as the conclusions are drawn from concrete and verifiable evidence used in the simulations.