Abstract
The emergence of online learning platforms means learners have a variety of learning behavior patterns. Many studies have found that there is a certain correlation between online learning behavior and learning performance. To better optimize the function of an online learning platform in hybrid teaching mode and further improve the quality of teaching and learning, this paper takes the 5y online learning platform as the target scene, and uses the online learning behavior data of 2205 learners and final exam score data as the breakthrough point of learning analytics. Through factor analysis on the behavior data of 13 measurement indicators of learners, this paper uses multiple linear regression model to analyze the correlation between learners’ online learning behavior and their final exam scores. The research found that the final examination results of learners are obviously positively correlated with the basic question factors and comprehensive question factors. Therefore, teachers and students who use 5y platform should focus on the use of knowledge point tests and unit tests to improve the quality of teaching and learning within the limited class time.
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References
LAK.: Shaping the future of the field. https://lak20.solaresearch.org/ (2020)
Muldner, K., Wixon, M., Rai, D., et al.: Exploring the impact of a learning dashboard on student affect. In: Int. Conf. Artificial Intelligence in Education, pp. 307–317. Springer International Publishing (2015). https://doi.org/10.1007/978-3-319-19773-9_31
Han, X.B., Huang, Y., Ma, J., et al.: A systematic review of learning analysis: review, identification and prospect. Research On Education Tsinghua University 38(03), 41–51+124 (2017)
Siemens, G., Long, P.: Penetrating the fog: analytics in learning and education. EDUCAUSE Review 46(5), 30 (2011)
Song, J., Feng, J.B., Qu, K.C.: Research on the influence of teacher-student interaction on deep learning in online teaching. China Educ. Technol. 11, 60–66 (2020)
Shen, X.Y., Liu, M.C., Wu, J.W., et al.: Research on MOOC learners’ online learning behavior and learning performance evaluation model. Distance Education in China (10), 1–8+76 (2020)
Liu, M., Zheng, M.Y.: Learning analysis and personalized resource recommendation in the view of intelligent education. China Educ. Technol. 09, 38–47 (2019)
Liu, F.H., Yi, X.T.: Analysis model construction and application research of online learning input. E-education Research 42(09), 69–75 (2021)
Mou, Z.J.: Multimodal learning analysis: learning analysis and analysis of new growth points. E-education Res. 41(05), 27–32+51 (2020)
Kent, C.,Cukurova, M.: Investigating collaboration as a process with theory- driven learning analytics. 7(1), 59–71 (2020)
Shen, Y.F.: A personalized learning path recommendation model based on multiple intelligent algorithms. China Educ. Technol. 11, 66–72 (2019)
Karthikeyan, V.G., Thangaraj, P., Karthik, S.: Towards developing hybrid educational data mining model (HEDM) for efficient and accurate student performance evaluation. Soft. Comput. 24(24), 18477–18487 (2020)
Wu, M.L.: SPSS statistics application practice: Questionnaire analysis and application statistics. Science Press, Beijing (2003)
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Li, C., Yao, J., Tang, Z., Tang, Y., Zhang, Y. (2023). The Influence of the Student's Online Learning Behaviors on the Learning Performance. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13421. Springer, Cham. https://doi.org/10.1007/978-3-031-25158-0_3
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