A Computational Strategy for Classification of Enem Issues Based on Item Response Theory

The National High School Examination (ENEM) gains each year more importance, as it gradually, replacing traditional vestibular. Many simulations are done almost randomly by teachers or systems, with questions chosen without discretion. With this methodology, if a test needs to be reapplied, it is not possible to recreate it with questions that have the same difficulty as those used in the first evaluation. In this context, the present work presents the development of an ENEM Intelligent Simulation Generation System that calculates the parameters of Item Response Theory (TRI) of questions that have already been applied in ENEM and, based on them, classifies them. in groups of difficulty, thus enabling the generation of balanced tests. For this, the K-means algorithm was used to group the questions into three difficulty groups: easy, medium and difficult. To verify the functioning of the system, a simulation with 180 questions was generated along the ENEM model. It could be seen that in 37.7% of cases this happened. This hit rate was not greater because the algorithm confounded the difficulty of issues that are in close classes. However, the system has a hit rate of 92.8% in the classification of questions that are in distant groups.