Past researches show that personality trait is a strong predictor for one's academic performance. Today, mature and verified marker systems for assessing personality traits already exist. However, marker systems-based assessing methods have their own limitations. For example, dishonest responses cannot be avoided. In this research, the goal is to develop a method that can overcome the limitations. The proposed method will rely on physiological signals for the assessment. Thirty participants have participated in this experiment. Based on the statistical results, we found that there are correlations between students' personality traits and their physiological signal change when learning via videos. Specifically, we found that participants' degree of extraversion, agreeableness, conscientiousness, and openness to experiences are correlated with the variance of heart rates, the variance of GSR values, and the skewness of voice frequencies, etc.