Correlation analysis of exercise and its vectorization method

GUO Na，LU Mei，ZHAO Xiang-jun

（School of Computer Science and Technology,Jiangsu Normal University,Xuzhou 221116,China）

Received:2015-11-26 Revised:2016-05-12 Online:2017-10-25 Published:2017-10-25

Abstract:

With the extensive application of Internet+ in education and the popularity of electronic exercises on mobile

devices, the learning data of students can be captured in real time. Mining the learning process data can

locate the students' weak points of knowledge, carry out targeted counseling prescriptions and push on-demand

knowledge, which has a positive impact on students in terms of reducing simple repetition and boosting

learning efficiency. We analyze the data of students' answer to the online exercises and propose a novel

vector representation of exercise. The correlation between exercises is captured by analyzing the data of

wrong answers to the exercise by different students and the exercise-to-vector (xcise2vec) model is

constructed. We also design a negative samples training algorithm and implement it in Linux. The exercise

vector can be obtained by training the data generated from the online system. Then, we can search for

similar, equivalent and interrelated exercises via exercise vector operation, and deduce other knowledge weak

links from the status of his answers. Experimental results verify the effectiveness of the proposed method.

Key words: exercise vector, exercise-to-vector(xcise2vec)model, on-demand knowledge, education intelligence

GUO Na，LU Mei，ZHAO Xiang-jun. Correlation analysis of exercise and its vectorization method[J]. Computer Engineering & Science.

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