Development of a Myers-Briggs Type Indicator Based Personalised E-Learning System - Adewale, Olumide; Agbonifo, Oluwatoyin; Osajiuba, Ojoma (International Journal of Computer (IJC), 2019)
The major challenge of the traditional learning system is space-time restriction and it is teacher-centred. The emergence of Information Technology gave rise for e-learning systems which are characterized with the components of teacher-centred and one-size-fits-all strategy. Subsequently, the concept of personalisation with learning technology was introduced that provides adaptation of learning contents to learning requirements of the learners. Hence, this research paper develops a personalised e-learning system that matches teaching strategy with learners’ learning style using Myers-Briggs Type Indicator (MBTI). The emphasis is laid on adaptive teaching strategy and revising the teaching strategy for the purpose of increasing learners’ learning performance. The mathematical model is developed for profiling learners to determine their learning style based on the MBTI questionnaire and Dynamic Bayesian Network is applied to revise the teaching strategy. The system is implemented using PHP and Wamp server and the database is designed using Structured Query Language (SQL). The developed system is tested using Undergraduate students studying Information Technology at Federal University of Technology, Minna. The percentage analysis of the students’ scores shows that 78% of students passed and the remaining 22% passed when the strategy was revised. The performance evaluation of the system is carried out and from the analysis it can be concluded that the Myers-Briggs Type Indicator Based Personalised E-learning System developed is appealing to students and the performance of students improved significantly.