02675nas a2200241   4500000000100000000000100001008004100002260001200043653004000055653001800095653002400113653002700137653002400164100001900188700001700207700001600224245015200240856005800392300000800450490001300458520194800471022001402419       9998                            d  c03/202510aClassroom Assessment Scoring System10aDeep Learning10aEmotional Contagion10aLong Short-Term Memory10aTeaching Evaluation1 aKuan-Cheng Lin1 aYa-Hsuan Lin1 aMu-Yen Chen00aA Realtime Classroom Assessment System for Analysis of Students’ Evaluation of Teaching Through a Deep Learning and Emotional Contagion Mechanism  uhttps://www.ijimai.org/journal/bibcite/reference/3566  a1-90 vIn press3 aStudent evaluations of teacher performance are often derived from end-of-semester assessments, significantly impacting the authenticity of teaching evaluations but failing to provide real-time feedback. In addition, teachers' emotional states affect student performance, including in terms of learning motivation and classroom participation, which reflect the students' emotional state. This teacher-student emotional contagion mechanism focuses on the interaction of teacher-student emotions and can be used to observe the quality of instructional performance. Therefore, automatically detecting teacher-student emotional interaction and then providing real-time class satisfaction feedback can provide teachers with a more effective basis for adjusting classroom content. This research proposes an end-to-end classroom real-time teaching evaluation system based on automatic facial-emotion recognition, which can accurately detect and directly analyze the emotions of students and teachers in streaming frames. The system consists of two parts: First, a YOLO model based on deep learning approaches is used to automatically detect the emotional states of teachers and students during the teaching process; Then, combining the emotional contagion mechanism with the teaching evaluation scale, teaching satisfaction can be predicted using a Long Short-Term Memory (LSTM) model to output a classroom satisfaction score within a fixed period. Further analysis of the testing dataset confirms that the model has a high reliability in predicting teaching satisfaction. Research results show the proposed system can achieve an emotional recognition accuracy rate of 98.1% for teachers and 99.5% for students based on the emotion
datasets. Further development could potentially provide teachers with strategies to improve classroom teaching effectiveness, better understand students' emotions and learning motivation, and improve learning outcomes.  a1989-1660