Chen Zixi, Shi Xiaolin, Zhang Wenwen, Qu Liaojian
Department of Counseling, Educational Psychology, and Special Education, Michigan State University, Lansing, MI, United States.
School of Hospitality and Tourism Management, College of Health and Human Sciences, Purdue University, West Lafayette, IN, United States.
Front Psychol. 2020 Jun 5;11:921. doi: 10.3389/fpsyg.2020.00921. eCollection 2020.
Teacher emotions are complex as emotions are unique to individuals, situated within specific contexts, and vary over time. This study contributed in synthesizing theories of the complexity in two characteristics of multi-dimensionality and dynamics. Further, we provided large-scale empirical evidence by employing big data and computational text analysis. The data contained around one million teachers' online posts from 2007 to 2018. It was scraped from three representative forums of teachers' workplace events and personal life occasions in a popular American teacher website. By conducting thread-level sentiment analysis in forums, we computed word-frequency-based eight discrete emotions ratios (i.e., anger, anticipation, disgust, fear, joy, sadness, surprise, and trust) and the degrees of sentiment polarity (i.e., positive, negative, and neutral). We then used latent Dirichlet allocation for topic classifications. These topics, proxies of contexts, covered a holistic range of teachers' real-life events. Some topics are in the main interest of scholars, such as teachers' professional development and students' behavioral management. This paper is also the first to include the less scholarly studied contexts like professional dressing advice and holiday choices. Then, we examined and visualized variations of emotions and sentiments across 30 topics along with three scales of time (i.e., calendar year, calendar month, and academic semesters). The results showed that teachers tended to have positive sentiments in the online professional community across the past decade, but all eight discrete emotions were presented. The compositions of the specific emotion types varied across topics and time. Regarding the topics of students' behavior issues, teachers' negative emotions' ratios were higher compared when it was presented in other topics. Their negative emotions also peaked during semesters. The forum of teachers' personal lives had positive emotions pronounced across topics and peaked during the wintertime. This paper summarized the evidenced multi-dimensionality characteristic with the multiple types of emotions as compositions and varying degrees of sentiment polarity of teachers. The dynamics characteristic is that teachers' emotions vary across contexts from their workplace to their personal lives and over time. These two characteristics of complexity also suggested potential interplay effects among emotions and across contexts over time.
教师的情感是复杂的，因为情感因人而异，存在于特定的情境中，并且会随时间变化。本研究有助于综合多维性和动态性这两个复杂性特征的理论。此外，我们通过运用大数据和计算文本分析提供了大规模的实证证据。数据包含2007年至2018年约100万教师的在线帖子。这些帖子是从美国一个热门教师网站上三个具有代表性的教师工作场所事件和个人生活场合的论坛中抓取的。通过在论坛中进行主题级情感分析，我们计算了基于词频的八种离散情绪比率（即愤怒、期待、厌恶、恐惧、喜悦、悲伤、惊讶和信任）以及情感极性程度（即积极、消极和中性）。然后，我们使用潜在狄利克雷分配进行主题分类。这些主题作为情境的代表，涵盖了教师现实生活事件的整体范围。一些主题是学者们主要感兴趣的，比如教师的专业发展和学生行为管理。本文也是首次纳入了像职业着装建议和假期选择等较少被学术研究的情境。然后，我们考察并可视化了30个主题中情感和情绪随三个时间尺度（即日历年、日历月和学术学期）的变化。结果表明，在过去十年中，教师在在线专业社区中倾向于有积极情绪，但八种离散情绪都有呈现。特定情绪类型的构成因主题和时间而异。关于学生行为问题的主题，与在其他主题中呈现时相比，教师负面情绪的比率更高。他们的负面情绪在学期期间也达到峰值。教师个人生活论坛在各个主题中都有明显的积极情绪，并且在冬季达到峰值。本文总结了以多种情绪类型作为构成以及教师情感极性程度不同所证明的多维性特征。动态性特征是教师的情绪在从工作场所到个人生活的不同情境中以及随时间而变化。复杂性的这两个特征还表明了情绪之间以及随时间跨情境的潜在相互作用效应。
