# Collaborative Analysis of Learners' Emotional States Based on Cross-Modal Higher-Order Reasoning
## 1 Introduction  
Currently, the new generation of information technology represented by artificial intelligence has become an important driving force for innovative changes in teaching. The new generation of information technology led by artificial intelligence is redefining and changing the connotations, structure, and ecology of education and teaching. Artificial intelligence technology helps to achieve personalized learning paths and precise recommendations for learning resources and can solve the problem of large-scale personalized teaching. In this context, the development of the Internet of Things sensing technology has made it possible to obtain large-scale cross-modal data, such as emotion recognition [1], behavior recognition [2], and video retrieval [3]. Based on the analysis of students' learning states using cross-modal teaching process data, it is possible to accurately grasp the individual state of each student, thereby helping to achieve large-scale and quality personalized education. Emotional learning is an important factor in students' learning states, and the recognition of students' learning emotions plays a crucial role in implementing personalized tutoring. Through analyzing students' non-verbal information such as facial expressions, tone of voice, and body language during classroom learning, it is possible to more accurately grasp students' learning motivations, emotional states, and cognitive loads, thus effectively identifying students' learning difficulties and emotional barriers [4]. This is crucial for promoting students' emotional well-being, enhancing their interest and efficiency in learning, and even stimulating their proactive learning enthusiasm. Furthermore, emotional recognition can also facilitate personalized teaching. Through deeply exploring theemotional states of all students, teachers can provide personalized guidance and support based on each student's individual circumstances. There have been many research results on the extraction of emotional states, such as methods based on local information. Firstly, facial keypoints or key regions were manually selected and local features were then extracted for expression classification ([PERSON] et al., 2004 [5]; [PERSON] et al., 2008 [6]; [PERSON] et al., 2009 [7]). Subspace feature extraction methods, focusing on the overall facial features, were employed to obtain the optimal transformation under certain criteria, projecting high-dimensional data into a low-dimensional space, retaining only discriminative features for facial expression classification in the subspace, thereby achieving the goal of dimensionality reduction, correlation elimination, and improved classification performance ([PERSON] et al., 2006). However, traditional emotion-recognition models mainly have relied on unimodal data, such as text or speech [8]. While this unimodal approach can provide useful emotion-recognition results in specific contexts, the accuracy of recognition results is not high. Unimodal emotion-recognition models often struggle to fully capture and understand students' emotional states. This is because emotional expression in students' learning processes is multidimensional, including language, tone of voice, facial expressions, and even body posture [9], These multimodal pieces of information collectively provide a more comprehensive reflection of individuals' emotional states. Therefore, through employing multimodal emotion recognition models, it is possible to significantly enrich the dimensions of educational assessment, enhance the quality and effectiveness of education and teaching, and achieve a more humane and intelligent educational environment. However, achieving more scientific, comprehensive, reasonable, and real-time personalized tutoring for learners based on cross-modal teaching process data still faces many difficulties. In many cases, the real facial images acquired in the classroom have numerous issues such as occlusion, small targets, image blurring, non-frontal faces, etc.