用生理信号预测学生性格，避免自报偏差
Do Students with Different Personality Traits Demonstrate Different Physiological Signals in Video-based Learning?
- 通过心率、皮肤电导、语音频偏等生理信号评估性格
- 30人实验发现外向、宜人性等与生理波动显著相关
- 适合教育科技、心理测量领域研究者参考
已有研究表明人格特质是学业表现的强预测因子。目前成熟的人格测评系统虽存在，但易受不实回答影响。本研究提出一种基于生理信号的人格评估新方法，以克服传统自评方式的局限性。实验招募30名参与者，通过视频学习任务采集其生理数据。统计分析显示，外向性、宜人性、尽责性及开放性等人格特质与心率方差、皮肤电导率（GSR）方差、语音频率偏度等生理指标存在显著相关性。
原文摘要 · Abstract (English)
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.
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