Exploring the solution space for adaptive curriculum sequencing: Study of a multi-objective approach

Adaptive Curriculum Sequencing (ACS) is an important issue in personalized learning. In ACS problems, one desires the best sequence of learning materials that meet the profile of a given student. To do so, multiple features of the students and the materials used are necessary to generate good solutions. In fact, understanding the students' goals, motivation, and preferences is not an easy task and, consequently, different Internet of Things (IoT) approaches to gather this information during the learning process have been proposed. Actually, some works from the literature consider five objectives and, in this case, one has a many-objective optimization problem. Instead of solving the optimization problem considering the multiple objectives individually, the usual approach is to obtain solutions for a weighted sum of the objective values using search approaches for mono -objective optimization problems. However, this kind of approach may bias the search and limits the capacity of finding good results. Here, we solve the multi -objective ACS problem considering five objective functions. NSGA-II, a wellknown Genetic Algorithm for multi -objective optimization problems, was used. In addition, the aggregation trees were employed to reduce the number of objectives to two and three due to the large number of objectives in the original problem. ACS problems from the literature were used to comparatively evaluate the proposed methods and the results obtained were compared to those found by the traditional approach of summing the objective values. According to these results, the best curriculum sequences were reached when using the proposal.