Selection of Mathematical Problems in Accordance with Student’s Learning Style

This article describes the implementation and development of an expert system as a support tool to tackle mathematical topics, by using Bayesian networks as engine of inference and a learning styles, as well as the difficulty level of problems and establish the base of the classifier probabilistic. The expert system makes decisions as which element to visualize at a specific moment, gives the student the best resource, and supervises the user progress. The article is divided into three sections, the first one deals with the construction of the expert system, the second one presents the operation of the system through the classification of students consistent with their profile, which is based on the prevailing learning style among them and in the difficulty level that problems have so that the student reaches in solving successfully. It also shows the operability of the system in respect of the allocation of digital resources in accordance with the identified profile and gradually provides more assignments with different difficulty levels, as the student progresses. An experimental study was performed by means of which the system was assessed under 30 students to the level of engineering and those who studied the Applied Calculus course in their second semester of the degree course. This group was named the study group (SG). The SG used the system for one semester. The results at the initial and final evaluation were from 3.58 to 7.37 for CG and SG respectively. Applying the F test, a statistically significant difference in increase was found (p <0.002). These results showed that SG identified the concept of derivative and applied that concept correctly in real problems solving correctly 74% of the final questionnaire, so it is concluded that the system expert opens a new way in educational research.