Artificial Intelligence Enabled Teaching Optimization for General Studies Elective Courses

Abstract

Network general knowledge course is a meaningful way to cultivate comprehensive quality talents in Chinese higher education based on the modern information technology environment, and the optimization of network general knowledge course teaching is directly related to whether the network general knowledge course can be effectively implemented, which affects the overall effect of talent cultivation. In this paper, starting from the purpose and goal of humanities general education courses, the optimization direction of teaching general education courses using artificial intelligence is clarified. Skeletal sequence feature extraction is used to obtain students’ general education classroom behavior data, and a stackable multimodal attention module consisting of a self-attention layer and a multimodal co-attention layer is designed for feature fusion and action prediction based on multimodal characteristics. After data integration and processing, the HGCL-LG network model is proposed to complete the diagnosis of students’ general knowledge cognition. The trend of interest assessment and multimodal model calculation value is consistent. In contrast, the student’s interest attention in general knowledge classroom above 0.33 accounts for more than 40% of the overall information, and the interest attention is good. Students’ scores on the objective questions of general knowledge are good; the average score range is between 1.5 and 2 points, the degree of dispersion is small, and the mastery of basic knowledge of general knowledge is good.

Keywords

• skeletal sequence feature extraction
• feature fusion
• hgcl-lg network model
• cognitive diagnosis
• multimodal model
• general knowledge course
• 68p30