Do Students with Different Personality Traits Demonstrate Different Physiological Signals in Video-based Learning?

TL;DR

This study explores how students' personality traits influence physiological signals during video-based learning, aiming to develop a more reliable assessment method that overcomes limitations of traditional marker systems.

Contribution

The paper introduces a novel physiological signal-based approach to assess personality traits in learning contexts, addressing issues of dishonesty in self-report methods.

Findings

Correlations between personality traits and physiological signals identified.

Physiological markers like heart rate variance and voice skewness relate to traits.

Method shows potential for more accurate personality assessment in education.

Abstract

Past researches show that personality trait is a strong predictor for ones 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.