An e-Learning System for Extracting Text Comprehension and Learning Style Characteristics

Technology mediated learning is very actively and widely researched, with numerous e-learning environments designed for different educational purposes developed during the past few decades. Still, their organization and texts are not structured according to any theory of educational comprehension. Modern education is even more flexible and, thus, demanding, requiring the combination of multiple educational theories for effective results. In this paper we present the combination of two educational theories for text comprehension and learning styles, that are in use by the newly developed Student Diagnosis, Assistance, Evaluation System based on Artificial Intelligence (StuDiAsE) an open learning system for unattended student diagnosis, assistance and evaluation based on artificial intelligence. Α trial test about the role of learning styles in student profiling for text comprehension in the educational environment StuDiAsE is described. Research was run with participation of students using the environment for prior knowledge test and text activities. The process revealed remarkable results about the role of learning styles in students’ profiles for text comprehension. Three dimensions for learning styles were identified: conceptualization, visualization and progression dimension and were used for profiling. Refinement of profiles incorporates learning styles by decoding student behavior, which reflects student learning styles for text comprehension.