Learning benefits of structural example-based adaptive tutoring systems

Abstract

This paper illustrates and evaluates a generic adaptive tutoring environment, structural example-based adaptive tutoring system (SEATS), based on the theory of cognitive knowledge acquisition. The system teaches by presenting side-by-side examples and highlighting their common structural components. This technique assists the process of generalization and reduces mapping by surface features, allowing students to apply their newly gained knowledge to different sets of problems. SEATS also implements adaptive presentation based on a straightforward model of student/user performance. SEATS was evaluated with a recursion tutorial used by 117 students in a 1-hour tutorial session. Results indicate that using adaptation in combination with the structural example-based feature produces an effect on rate and extent of learning significantly greater than when the features are used alone, or when both are absent. The study further points out that future evaluations will have to take students' curiosity into account, since many of them are likely to give at least some incorrect answers on purpose, to test the response of the system. The results indicate that building SEATS with simple adaptive mechanisms is an efficient way of teaching electronically.