How to Present Example-Based Support Adaptively in Intelligent Tutoring Systems

摘要

Previous research investigated the effectiveness of Problem Solving (PS), Worked Examples (WE) and Erroneous Examples (ErrEx) with different types of learners. However, there is still no agreement on what kind of learning support (in terms of different learning activities) should be provided to students in Intelligent Tutoring Systems (ITSs) to optimize learning. A previous study found that alternating worked examples and problem solving (AEP) was the best learning strategy compared with worked examples only or problem solving only in the domain of SQL-Tutor. In our recent study, we found that erroneous examples in addition to worked examples and problem solving aid learning in the same domain. The goal of this PhD project is to investigate how SQL-Tutor could maximize learning by adaptively presenting PS, WE or ErrEx to students based on their models.