Work

An Intelligent Tutoring System with Variable Levels of Instructional Support for Instructing Natural Deduction

Year: 2014

Type: article

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.

Source: Transactions of the Japanese Society for Artificial Intelligence

DOI: 10.1527/tjsai.29.148

Authors: Kazuhisa Miwa, Hitoshi Terai, Nana Kanzaki, Ryuichi Nakaike

Institutions: Nagoya University, Nagoya Women's University, Kyoto University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: diamond

Funder: Japan Society for the Promotion of Science