Data-Driven Hint Generation in Intelligent Tutoring Systems for Learning Microeconomics

Abstract

Scientific inquiry skills is used in all educational areas. In the context of microeconomics, it is widely known that the development of scientific inquiry skills is central to the construction of ideas that enable understanding. Intelligent tutoring systems (ITSs) have been shown to be effective in supporting students' domain-level learning through guided problem solving practice. ITSs provide personalized feedback (in the form of hints) to students and improve learning at effect sizes approaching that of human tutors. However, creating an ITS to adapt to individual students requires the involvement of experts to provide knowledge about both the academic domain and novice student behavior in that domain’s curriculum. Creating an ITS requires time, resources, and multidisciplinary skills. Because of the large possible range of problem solving behavior for any individual topic, the amount of expert involvement required to create an effective, adaptable tutoring system can be high, especially in open-ended problem solving domains. Data-driven ITSs have shown much promise in increasing effectiveness by analyzing past data in order to quickly generate hints to individual students. However, the fundamental long term goal was to develop “better, faster, and cheaper” ITSs. The main goal of this paper is to: 1) presents ITSs used in the microeconomics education; and 2) introduce data-driven ITSs for microeconomics education.