A Study on Personalized Identification and Evaluation of Higher Vocational Teaching Based on Multimodal Data

The multimodal learning evaluation aims to combine data from two or more sources, and use data mining analysis methods to objectively evaluate complex learning behaviors and analyses them in depth. Based on this, the paper firstly designed a multimodal data-driven blended learning evaluation framework for higher vocational education, and relied on this framework to capture multimodal learning data of students participating in higher vocational education in a blended learning environment through automatic recording, online platform data collection, testing and other means; The results show that the combination of the variance selection method and the decision tree classification algorithm can achieve the best evaluation results. The chapter learning, video learning duration, topic discussion in online learning and attention and study notes in offline learning are the best evaluation features to evaluate students' blended learning, among which the video learning duration has the greatest impact on the final test score. The article is important for improving the evaluation of personalized and blended learning in higher vocational education and reflecting the role of evaluation in improving the quality of higher vocational education.