Development and Evaluation of a Learning Level Prediction Model Based on the Growth Curve About the Long-Term Learners

Abstract

There are measurement devices such as magnetic resonance imaging (MRI), which can acquire brain activity. The case of MRI can use to investigate brain function. In addition, other physiological measurement techniques are used as supportive measures in this area of research. Various studies on the learning process have been carried out using these instruments. However, long-term measurement is hard for participants and researchers. The multiple measurements of short periods are consecutive mostly. Concerning this style, the medical risk of these measurements has been pointed out. In this case, one solution is measuring the learning level offline, mean outside MRI. It is considered to reduce the physical and psychological strain on the participants. In this study, we conducted an experiment to observe how to learn about solving puzzles over a long period of time. The obtained learning curve shows the possibility to choose the best period to measure on MRI. Based on these data, we propose a model for predicting the learning level. From these models, we discuss the appropriate timing for biometric measurements in terms of the learning level and validate the curve models.