# A Novel Method for Cross-Modal Collaborative Analysis and Evaluation in the Intelligence Era
## 1 Introduction
### Proposition of Research Questions  
Education in the age of intelligence emphasizes the cultivation of students' abilities in terms of innovation, interdisciplinary problem-solving, teamwork, and other comprehensive qualities through the process of hands-on practice. The biggest difference compared with traditional education is that it focuses more on the process of learning than the ultimate results. The emotional status generated by students during the learning process can, to some extent, reflect their learning interests and the learning effects, and therefore form the mechanism of learning eventually. The status can be recorded and analyzed through cross-modal data such as facial expressions, actions, language, and heartbeat.  
Cross-modal collaborative analysis and evaluation can provide an effective technical solution for the construction of a teaching evaluation system in the Age of Intelligence. Thus, the data analysis and conclusions from the evaluation system can shed some light on research questions such as how to improve the quality of intelligent education.  
In the smart education environment, based on technologies such as the Internet of Things and artificial intelligence, as well as intelligent sensing devices such as eye trackers and electroencephalgraphs, it is possible to monitor and record complex teaching processes in real-time and form cross-modal data such as audios, texts, images, videos, etc. Based on cross-modal learning analysis technology, collaborative analysis of student learning behavior data is carried out to mine and restore the students' learning emotional status information implied by the cross-modal and accompanying behavioral information in the classroom. A scientific model further reveals the implicit relationship between teaching and learning process information and classroom education quality, and a new evaluation model of classroom teaching and learning has been constructed. This evaluation method is more comprehensive and scientific by eliminating the weaknesses of the traditional educational evaluation model, which emphasized tests and grades, and by optimizing teaching methods based on evaluation and improving the quality of classroom teaching. The purposed method focuses on evaluating and developing students' innovative thinking ability, comprehensive quality, personality, and mental health.  
Taking the cross-modal learning behavior data as the carrier, with technologies such as artificial intelligence, big data, etc., this research collects students' emotional status through the whole process of learning, conducts a collaborative analysis of learning behavior, and thus constructs a new evaluation method of teaching that links learning behavior with learners' emotional status. This research indicates significance in research and real-world practice in terms of achieving a comprehensive and accurate assessment of a student's ability, cognitive level, personality traits, mental health, etc., explaining and exploring cross-modal data-driven educational phenomena and educational laws, offering guidance on the optimization of the teaching process, and improving the goal of classroom teaching quality.