Differences in brain signal complexity between experts and novices when solving conceptual science problem: a functional near-infrared spectroscopy study.

Assessing the result of conceptual change (i.e., whether an individual has come to correctly understand a science concept) is important in science education, however traditional assessment methods lack objectivity. In this study, permutation entropy (PE) based complexity, a kind of entropy used to quantify the complexity describing the uncertainty of time series, was explored by the functional near-infrared spectroscopy to seek an objective neurobiological indicator for this assessment. Two groups of participants, engineering students (classified as "experts") and humanities students (classified as "novices"), were tested on their conceptions to discriminate the speed of cars according to the animation, while the hemodynamic response was recorded over their inferior frontal gyrus (IFG). The activation analysis, PE based complexity analysis, and k-means clustering analysis were conducted. The results indicated that experts performed the task better than novices in behavioral performances, and PE values in the IFG were smaller for experts, especially in the right IFG. Furthermore, the k-means clustering analysis showed that the PE could be a feature to classify the students into two groups. It is concluded that the PE is a promising neurobiological indicator for assessment of this kind.