标题： 个性化编程教育：基于学生性格特征使用机器学习提高学习效果 显示英文标题

标题： Personalized Programming Education: Using Machine Learning to Boost Learning Performance Based on Students' Personality Traits

摘要： 研究表明，人格与成就有关，已经开发了几种人格评估模型。 然而，大多数要么是问卷形式，要么基于标记系统，这带来了局限性。 我们提出了一种基于生理信号的模型，从而确保数据的客观性并防止不可靠的回答。 从台湾元智大学电机工程系招募了30名参与者。 使用可穿戴传感器在参与者观看并总结视频时收集生理信号。 然后他们完成了一个基于大五因素标记系统的个性问卷。 结果用于构建个性预测模型，结果显示皮肤电反应和心率方差是预测外向性的关键因素；心率方差也预测了宜人性和尽责性。 该实验的结果可以阐明学生的个性特征，这有助于教育者选择适当的教学方法。 显示英文摘要

摘要： Studies have indicated that personality is related to achievement, and several personality assessment models have been developed. However, most are either questionnaires or based on marker systems, which entails limitations. We proposed a physiological signal based model, thereby ensuring the objectivity of the data and preventing unreliable responses. Thirty participants were recruited from the Department of Electrical Engineering of Yuan Ze University in Taiwan. Wearable sensors were used to collect physiological signals as the participants watched and summarized a video. They then completed a personality questionnaire based on the big five factor markers system. The results were used to construct a personality prediction model, which revealed that galvanic skin response and heart rate variance were key factors predicting extroversion; heart rate variance also predicted agreeableness and conscientiousness. The results of this experiment can elucidate students personality traits, which can help educators select the appropriate pedagogical methods.