ABSTRACT
The course of Principles of Communications involves a wide range of know ledge and is both theoretical and practical. It is difficult for beginners to closely link theory with practice. Therefore, it is necessary to consolidate theoretical teaching results through experiments. The traditional experimental teaching of Principles of Communications is to let students go to the laboratory and carry out experiments by programming on software radio hardware. However, during the COVID-19 pandemic period, students are unable to conduct experiments in the laboratory, so online experiments become the only feasible scheme. This paper introduces an online experiment scheme, compares the online scheme with lab experiments. The online experiment system has been used in one class of 28 students and the positive rate of the system from the students is 86%.
ABSTRACT
With the vast applications of massive online learning platforms during the coVID-19 outbreak, the personalized exercise recommendation methods play an import role on computer aided instruction(CAI). Most existing methods generates the exercises according to the contents and knowledge system structure, lacking semantic relationships between exercises and its knowledge. Knowledge graph is widely used to represent the semi-structured and schemaless information (nodes) and their relation (edges), and indicate the sentence embedding grammatical structure and semantic relations, thus it can be applied on computer aided instruction to automatically generate the personalized exercises. Aiming to improve the efficiency of exercise recommendation, this paper studies the feature information of computer network course, and proposes a content and knowledge graph based personalized exercise recommendation method. More specifically, knowledge graph is firstly constructed from entities and relations of computer network course, and the information vectors of exercises are generated by combining the knowledge with the exercises content. And then the learner's historical log data is analyzed, and the semantic similarity between exercises and their knowledge are generated for the wrong answers. According the semantic similarity of knowledge, the final exercises are recommended for the learners. Experimental results show that the proposed method can improve the efficiency of exercises recommendation. © 2020 IEEE.