Abstract
The global explosion of COVID-19 has brought unprecedented challenges to traditional higher education, especially for freshmen who have no major; they cannot determine what their real talents are. Thus, it is difficult for them to make correct choices based on their skills. Generally, existing methods mainly mine isomorphic information, ignoring relationships among heterogeneous information. Therefore, this paper proposes a new framework to give freshmen appropriate recommendations by mining heterogeneous educational information. This framework is composed of five stages: after data preprocessing, a weighted heterogeneous educational network (WHEN) is constructed according to heterogeneous information in student historical data. Then, the WHEN is projected into different subnets, on which metapaths are defined. Next, a WHEN-based embedding method is proposed, which helps mine the weighted heterogeneous information on multiple extended metapaths. Finally, with the information mined, a matrix factorization algorithm is used to recommend learning resources and majors for freshmen. A large number of experimental results show that the proposed framework can achieve better results than other baseline methods. This indicates that the proposed method is effective and can provide great help to freshmen during the COVID-19 storm.
Highlights
COVID-19 has broken out in more than 200 countries around the world, causing millions of people to become infected and die [1], and it has become a major globalThe associate editor coordinating the review of this manuscript and approving it for publication was Davide Patti .public health event
Freshmen can quickly find their talents. This framework is mainly composed of the following parts: first, to effectively integrate heterogeneous information in the field of education, this paper proposes a weighted heterogeneous educational network (WHEN); second, a series of semantically rich extension metapaths are defined in WHEN to realize multiple data mining tasks; third, a graph embedding method is used to learn the representation of individual students and recommended items, and a new random walk is proposed; and it combines the learned representation and matrix decomposition algorithm to recommend learning resources and majors for freshmen
RELATED WORK we summarize the relevant work from three aspects: general enrollment, educational data mining, and graph embedding-based recommendation
Summary
COVID-19 has broken out in more than 200 countries around the world, causing millions of people to become infected and die [1], and it has become a major global. This framework is mainly composed of the following parts: first, to effectively integrate heterogeneous information in the field of education, this paper proposes a WHEN; second, a series of semantically rich extension metapaths are defined in WHEN to realize multiple data mining tasks; third, a graph embedding method is used to learn the representation of individual students and recommended items, and a new random walk is proposed; and it combines the learned representation and matrix decomposition algorithm to recommend learning resources and majors for freshmen. The main innovations and contributions of this paper can be summarized as follows: 1) In this paper, we propose a new framework that can help freshmen who are not divided into majors to identify their talents and recommend suitable majors and learning materials This will effectively reduce the impact of COVID-19 on students.
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