高校教师心理健康与工作能力的关系:情感分析算法的应用

Xiaobin Wu, Lizheng Zhuo
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引用次数: 0

摘要

在系统收集和调查与教学领域相关的数据时,教师心理健康(MH)与工作能力(JC)之间的关联是一个尚未系统研究的重要领域。了解这种关联至关重要,因为它直接影响到教育质量(QoE)及其标准教育体系。由于在评估所有定性数据(如开放式调查反馈(OESF))方面存在问题,传统模型经常发现,要准确表征错综复杂的关系具有挑战性。本文通过引入混合方法(MMA)来报告这些任务。该研究通过中国三省众多高等院校的在线学习进行指导,并建议将定量研究数据与定性情感分析(SA)相结合进行分析。该方法的新颖之处在于,它提出了一种基于潜狄利克特分配(LDA)的SA算法,可对OESF问题中的所有定性数据进行研究。这种算法对教师的 MH 及其与他们的专业技能之间的联系提出了更哲学化的认识。这项研究的结果是对与高校教师(CT)相关的 MH 与 JC 之间动态关系的重要见解。它强调了每个特征如何影响其他特征,以及支持教师幸福的教育战略的先决条件。通过克服现有模型中的界限,所提出的工作有助于更广泛、更好地了解教师福祉、其对教育质量的影响以及 SA 在教育研究中的潜力。我们比较了三种分类器--奈夫贝叶斯(NB)、支持向量机(SVM)和线性回归(LR)--在本 SA 研究选定的六个主题上的表现。评估的性能分析包括准确度、精确度、召回率和 F1 分数。通过 Cronbach's alpha 值确认了 OESF 测量的可靠性,表明其具有较高的内部一致性:JC 为 0.85,MH 状态为 0.88,创新教学能力为 0.82。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Relationship between mental health and job competence of college teachers: Application of sentiment analysis algorithm
In systematically collecting and investigating data related to the teaching field, the association between teachers’ Mental Health (MH) and Job Competence (JC) is an important field that has not been systematically studied. Understanding this association is vital due to its direct impact on the Quality of Education (QoE) and its standard educational system. Due to problems in evaluating all qualitative data, such as Open-Ended Survey Feedback (OESF), traditional models frequently find it challenging to epitomize intricate relations accurately. This article reports on these tasks by introducing a Mixed-Method Approach (MMA). The research study was directed through online learning across numerous higher education institutions in three provinces of China, and a combination analysis of quantitative study data using qualitative Sentiment Analysis (SA) was recommended. The new thing about the method is that it suggests an algorithm based on Latent Dirichlet Allocation (LDA) to SA that lets all of the qualitative data from the OESF questions be studied. This algorithm suggests a more philosophical knowledge of teachers’ MH and its association with their specialized skills overall OESF. The study’s results represent a significant insight into the dynamics between MH and JC related to College Teachers (CT). It highlights how every feature impacts others and the prerequisites for educational strategies supporting teacher well-being. By overcoming boundaries in existing models, the proposed work contributes to a broader and better knowledge of teacher well-being, its impact on educational quality, and the potential for SA in educational research. We compared how well three classifiers—Naïve Bayes (NB), Support Vector Machine (SVM), and Linear Regression (LR)—performed on six topics that were chosen for this SA research. The performance analysis for evaluation is accuracy, precision, recall, and F1-score. The reliability of the OESF measures was confirmed with Cronbach’s alpha values signifying high internal consistency: 0.85 for JC, 0.88 for MH status, and 0.82 for innovative teaching ability.
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