An Empirical and Machine Learning Analysis of Emotional Experiences in College English Translation Courses with Online Platforms

Emotion experience in foreign language learning. Emotional experience directly influences students’ learning outcomes. College English translation is a high-level course. This course is highly complicated and professional. The students also have different emotional experiences. So this study takes Yicat online translation platform for teaching. Questionnaire surveys are carried out to collect data in this study. Non-English major students in a Wuhan university in China are the analysis targets. Anxiety, enjoyment and boredom are the targeted emotional factors for analysis. The study purpose is to figure out which emotional factors significantly influence the translation learning result. To ensure the study objective and novelty, support vector machine (SVM) method is applied in this study to analyze the data and get the main emotion factors that influence the translation performance. And it is to show the connections among the anxiety, the enjoyment and the boredom. From the final results, some valuable suggestions are provided for improving the teaching approach and making the translation courses more effective.