Adaptive Teaching System, Personalized Teaching Strategies Research

Web-based instructional systems seldom concern learner's knowledge background, learning object, cognize style, preference etc. The content of these systems cannot be changed based on learner's individual characteristic. Therefore, we investigate ways in which personalized instruction can be delivered via adaptive intelligence instructional system.This study creates a prototype of I-Tutor. The system can use machine learning to know learner's knowledge level and preferences, thus can give individualize instructional strategy. I-Tutor can consider those differences and adapt the information presented to each user. This adaptive content is achieved through a two phase approach which considers the user's level of understanding and the content that matches the user's preferences. A Naive Bayes Classifier is used to learn the student's preferences by observing what type of content he chooses to see.An empirical study of the I-Tutor System was conducted. Results from this study show distinct differences in students' learning styles and provide evidence that using the same teaching strategies for each student cannot adequately support all students. This is demonstrated through two examples. The first shows that there is not a consistent direction for the correlation between time spent studying and quiz performance. The second shows that using the same parameters for the Naive Bayes Classifier for...