This paper presents estimation of expert-novice levels based on eye tracking and motion data obtained while engineers inspect a subway tunnel. We first obtain eye tracking and motion data via wearable sensors measured when engineers inspect the subway tunnel. Next, we demonstrate that features extracted from eye tracking and motion data can effectively represent differences of the expert-novice levels. Finally, we construct several classification models that estimate the expert-novice levels of the tunnel inspection based on the extracted features. Experimental results show that obtained eye tracking and motion data are effective for the estimation of the expert-novice levels.