Learning Style Identification by CHAEA Junior Questionnaire and Artificial Neural Network Method: A Case Study

By the lack of personalization in education, students obtain low performance in different subjects in school, particularly in mathematics. Therefore, learning style identification is a crucial tool to improve academic performance. Although traditional methods such questionnaires have been extensively used to the learning styles detection in youths and adults by its high precision, it produces boredom in children and does not allow to adjust learning automatically to student characteristics and preferences over time. In this paper, two methods for learning style recognition: CHAEA-Junior questionnaire (static method) and Artificial Neural Networks (automatic method) are explored. The data for the second technique used answers from the survey and the percentage scores from mathematical mini-games (Competitor, Dreamer, Logician, Strategist) based on Kolb’s learning theory. To the validity between both methods, it was conducted a pilot study with primary level students in Ecuador. The experimental tests show that Artificial Neural Networks are a suitable alternative to accurate models for automatic learning recognition to provide personalized learning to Ecuadorian students, which achieved close detection results concerning CHAEA-Junior questionnaire results.