Learning Analytics to Adaptive Online IRT Testing Systems "Ai Arutte" Harmonized with University Textbooks

Item response theory (IRT) provides more accurate and fairer evaluations of individual abilities than classical test theory does, and thus the IRT has gradually been recognized as one of the proper evaluation methodologies in many testing fields. Teaching using textbooks works in university education as well as self-studying using online learning systems. However, they have not been connected each other. Thus, to utilize both beneficial sides of textbooks and the internet system, we propose a new learning style using textbooks and online testing for exercises using the adaptive online IRT testing systems, called Ai Arutte. In this paper, we introduce a new use of the adaptive online IRT testing system to assist self-learning in studying undergraduate subject, Linear Algebra, and we show its learning analytics. The combination of a mathematical textbook and the adaptive online IRT system works well. Ai Arutte case shows that students feel this kind of self-studying is fun and interesting.