Big Data Course Multidimensional Evaluation Model based on Knowledge Graph enhanced Transformer - Ning Liu; Yeyangyi Xiang; Fei Wang; Shuyu Cao (Cognitive Robotics)
Based on the positioning of training application-oriented and innovative talents in the field of big data, this article aims to address the current situation where the theoretical system of big data course is not complete, the experimental system is unreasonable, and the assessment indicators are not perfect. A Transformer based “1 + 1 + N” big data course unified system and multidimensional evaluation model is constructed, reforms and practices are carried out in terms of improving the course theoretical system, increasing unit experiments and comprehensive experiment cases, and improving process assessment. The Transformer based multi-dimensional evaluation model of the big data course is proposed to solve the current problems of heavy theory and light practice, heavy standardization assessment and light innovation ability training in the course. The proposed course unified system and multidimensional evaluation model had achieved remarkable results, effectively increasing students’ construction of the big data professional knowledge system, enhancing students’ subjective initiative in learning the course, and significantly improving students’ innovative ability and ability to comprehensively solve practical problems.