Personalized Knowledge Map Recommendations based on Interactive Behavior Preferences

doi:10.23940/ijpe.21.01.p4.3649

Int J Performability Eng ›› 2021, Vol. 17 ›› Issue (1): 36-49.doi: 10.23940/ijpe.21.01.p4.3649

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Personalized Knowledge Map Recommendations based on Interactive Behavior Preferences

Zhaoyu Shou^a^b^*, Yiru Wen^a, Pan Chen^a, and Huibing Zhang^c

^aSchool of Information and Communication, Guilin University of Electronic Technology, Guilin, 541000, China
^bKey Laboratory of Cognitive Radio and Information Processing Ministry of Education, Guilin, 541000, China
^cGuangxi Key Laboratory of Trusted Software, Guilin, 541000, China

Submitted on ; Revised on ; Accepted on
Contact: * Corresponding author. E-mail address: guilinshou@guet.edu.cn
About author:
Zhaoyu Shou received his Ph.D. degree in computer science and technology from Guilin University of Electronic Technology. His research interests cover outlier detection, computer information management, and education big data.
Yiru Wen is a master's student in the School of Information and Communication at Guilin University of Electronic Technology. Her research interests include the analysis of personalized recommendation algorithm, the construction and analysis of knowledge map, and the theory and algorithm of educational big data.
Pan Chen is a master's student in the School of Information and Communication at Guilin University of Electronic Technology. His research interests include the analysis of learning emotions.
Huibing Zhang received his Ph.D. in computer science and technology from the Beijing University of Technology. He is an associate professor in the Guangxi Key Laboratory of Trusted Software at Guilin University of Electronic Technology. His research interests include educational big data, Internet of things, and social computing.
Supported by:
the following foundations: the National Natural Science Foundation of China (No61967005, 61662013, U1501252), Innovation Project of GUET Graduate Education (No2020YCXS022), and Key Laboratory of Cognitive Radio and Information Processing Ministry of Education (NoCRKL190107)

Abstract

Abstract:

In order to achieve personalized learning in an online learning environment, this paper proposes a personalized knowledge map recommendation algorithm based on interactive behavior preferences through collaborative analysis of learners' online interactive behavior data. First, it defines the learners' interaction degree of knowledge points based on the interactive behavior in the online learning process, the learner's mastery degree of knowledge points based on the online test scores, and the learning effect in combination with the interaction degree of knowledge points. Secondly, this paper proposes a correction factor based on the stability of difference in the interaction degree of knowledge points, combined with the average interaction degree of knowledge points to improve the similarity calculation model. According to the MAE and MSE evaluation indexes of similarity prediction, the prediction effect of the similarity calculation model is evaluated. Finally, combined with the learning effect and similarity calculation model, the knowledge map is recommended for the target learners. The validity of the recommendation is proven by the F1 value and MAE evaluation index.

Key words: interactive behavior, interaction degree of knowledge points, similarity calculation model, knowledge map

Zhaoyu Shou, Yiru Wen, Pan Chen, and Huibing Zhang. Personalized Knowledge Map Recommendations based on Interactive Behavior Preferences [J]. Int J Performability Eng, 2021, 17(1): 36-49.

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Personalized Knowledge Map Recommendations based on Interactive Behavior Preferences

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