Equipping children elearning systems with a hybrid personality type indicator

Abstract

Providing adaptivity for eLearning systems may be accomplished through incorporating learning style, which may be supposed non-stable characteristic in case of children. This paper presents a hybrid model for initiating and updating personality type of children in eLearning systems. A modified MMTIC (Murphy-Meisgeier Type Indicator for Children) questionnaire has been applied in start-up phase of system to recognize children personality type. This questionnaire is made based on MBTI (Myers-Briggs Type Indicator). Patterns of children behaviors are extracted by monitoring the details of their interaction with system. Using clustering algorithms and sequential pattern mining, system updates the personality type of children. The proposed approach is used in 81 fourth-grade children in elementary school. Delivery results suggest that this method provides good precision in diagnosing children personality type and can be an appropriate solution for non-stability in children learning style.