An intelligent tutoring system with variable levels of instructional support for instructing natural deduction

Abstract

We present an intelligent tutoring system that teaches natural deduction to undergraduate students. An expert problem solver in the system provides basic instructional help, such as suggesting the use of a rule in the next step of solving a problem and indicating the inference drawn by applying the rule. The system provides help by using a complete problem solver as an expert instructor. Students learning with our tutoring system can vary the degree of help they receive (from low to high and vice versa). Empirical evaluation showed that the system enhanced the problem-solving performance of participants during the learning phase, and these performance gains were carried over to the post-test phase. The analysis of participants' interactions with the system revealed the between-participants adaptation of students, meaning that participants with lower scores learned using higher levels of assistance than those with higher scores. In addition, the analysis revealed the within-participants adaptation of students, meaning that they adaptively changed levels of support according to their learning progress and the degree of difficulty of the problem. © The Japanese Society for Artificial Intelligence 2014.