A Personality-Based Virtual Tutor for Adaptive Online Learning System
Mary Jane C. Samonte, Gabriel Edrick Acuna, Luis Antonio Z. Alvarez, Jeffrey M. Miraflores
Abstract
E-learning has become one of the most extensively used electronic systems in the field of education. Despite its benefits, there are some capabilities and concerns that may have a negative impact on students’ performance. As a result, personalized e-learning systems are being developed, which adapt e-learning systems to the users’ personality, knowledge, behavior, interests, or preferences. This will improve the overall learning experience and performance of the students. This study created and tested an e-learning system, called “Cybele” to help students learn cybersecurity in an online mode of learning. “Cybele” is a personality-based virtual instructor for cybersecurity online learning that includes a chatbot built using Rasa Open Source. The paper used Myers-Briggs Type Indicator (MBTI) personality model for initial learner assessment to address various student learning styles for a better online learning experience. Testing was done for the system functionality and the traditional learning approach was compared to the personalized e-learning system. Results show that students who participated in the developed adaptive e-learning environment performed better than those who pursue the traditional learning method.