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Machine Learning Theory and Practice, 2022, 3(4); doi: 10.38007/ML.2022.030403.

Online Education User Behaviour Based on Machine Learning


Yanan Li

Corresponding Author:
Yanan Li

Liaoning Metallurgical Vocational and Technical College, Liaoning, China


The rapid development of machine learning has made it possible to quickly mine exploitable data from massive amounts of data, and it is important for online education platforms to reasonably track the changing learning behaviour of learners. The aim of this paper is to study the prediction of online education user behaviour based on machine learning. A deep clustering algorithm is proposed based on demographic information about learning behaviour and clickstream data recorded in a university virtual learning environment. Applying the deep clustering model proposed in this paper to the analysis of the distribution of learning activities and the correlation between learning behaviour and education level, it is demonstrated that the deep clustering model proposed in this paper's can analyse and predict the learning behaviour of different student groups. It helps us to further study and analyse students' learning behaviour in depth and comprehensively in order to provide real-time quality feedback and promote the development of online education.


Machine Learning, Online Education, User Behaviour, Behaviour Prediction

Cite This Paper

Yanan Li. Online Education User Behaviour Based on Machine Learning. Machine Learning Theory and Practice (2022), Vol. 3, Issue 4: 18-26. https://doi.org/10.38007/ML.2022.030403.


[1] Vashek Matyas, Kamil Malinka, Lydia Kraus, Lenka Knapova, Agata Kruzikova: Even if users do not read security directives, their behavior is not so catastrophic. Commun. ACM 65(1): 37-40 (2022) https://doi.org/10.1145/3471928

[2] R. Geetha, S. Karthika, Ponnurangam Kumaraguru: 'Will I Regret for This Tweet?' - Twitter User's Behavior Analysis System for Private Data Disclosure. Comput. J. 65(2): 275-296 (2022) https://doi.org/10.1093/comjnl/bxaa027

[3] Noureddine Amraoui, Belhassen Zouari: Anomalous behavior detection-based approach for authenticating smart home system users. Int. J. Inf. Sec. 21(3): 611-636 (2022) https://doi.org/10.1007/s10207-021-00571-6

[4] Husna Sarirah Husin, James A. Thom, Xiuzhen Zhang: Evolution of user navigation behavior for online news. Int. J. Web Inf. Syst. 18(1): 1-22 (2022) https://doi.org/10.1108/IJWIS-06-2021-0064

[5] Lori Baker-Eveleth, Robert W. Stone, Daniel M. Eveleth: Understanding social media users' privacy-protection behaviors. Inf. Comput. Secur. 30(3): 324-345 (2022) https://doi.org/10.1108/ICS-07-2021-0099

[6] Jongpil Park, Jai-Yeol Son, Kil-Soo Suh: Fear appeal cues to motivate users' security protection behaviors: an empirical test of heuristic cues to enhance risk communication. Internet Res. 32(3): 708-727 (2022) https://doi.org/10.1108/INTR-01-2021-0065

[7] Saeid SadighZadeh, Marjan Kaedi: Modeling user preferences in online stores based on user mouse behavior on page elements. J. Syst. Inf. Technol. 24(2): 112-130 (2022) https://doi.org/10.1108/JSIT-12-2019-0264

[8] Malvika Singh, Babu M. Mehtre, S. Sangeetha: User behavior based Insider Threat Detection using a Multi Fuzzy Classifier. Multim. Tools Appl. 81(16): 22953-22983 (2022) https://doi.org/10.1007/s11042-022-12173-y

[9] Sylvia Chan-Olmsted, Rang Wang: Understanding podcast users: Consumption motives and behaviors. New Media Soc. 24(3): 684-704 (2022) https://doi.org/10.1177/1461444820963776

[10] E. Karthik, T. Sethukarasi: Sarcastic user behavior classification and prediction from social media data using firebug swarm optimization-based long short-term memory. J. Supercomput. 78(4): 5333-5357 (2022) https://doi.org/10.1007/s11227-021-04028-4

[11] Mahyar Kamali Saraji, Abbas Mardani, Mario Köppen, Arunodaya Raj Mishra, Pratibha Rani: An extended hesitant fuzzy set using SWARA-MULTIMOORA approach to adapt online education for the control of the pandemic spread of COVID-19 in higher education institutions. Artif. Intell. Rev. 55(1): 181-206 (2022) https://doi.org/10.1007/s10462-021-10029-9

[12] Jaroslav Majerník, Andrea Kacmarikova, Martin Komenda, Andrzej A. Kononowicz, Anna Kocurek, Agata Stalmach-Przygoda, Lukasz Balcerzak, Inga Hege, Ioan-Adrian Ciureanu: Development and implementation of an online platform for curriculum mapping in medical education. Bio Algorithms Med Syst. 18(1): 1-11 (2022) https://doi.org/10.1515/bams-2021-0143

[13] Steven J. Greenland, Catherine Moore: Large qualitative sample and thematic analysis to redefine student dropout and retention strategy in open online education. Br. J. Educ. Technol. 53(3): 647-667 (2022) https://doi.org/10.1111/bjet.13173

[14] Kyungmee Lee, Mik Fanguy: Online exam proctoring technologies: Educational innovation or deterioration? Br. J. Educ. Technol. 53(3): 475-490 (2022) https://doi.org/10.1111/bjet.13182

[15] Caleb Or, Elaine Chapman: Development and validation of an instrument to measure online assessment acceptance in higher education. Br. J. Educ. Technol. 53(4): 977-997 (2022) https://doi.org/10.1111/bjet.13180

[16] Henriikka Vartiainen, Hanna Vuojärvi, Kaija Saramäki, Miikka Eriksson, Ilkka Ratinen, Piritta Torssonen, Petteri Vanninen, Sinikka Pöllänen: Cross-boundary collaboration and knowledge creation in an online higher education course. Br. J. Educ. Technol. 53(5): 1304-1320 (2022) https://doi.org/10.1111/bjet.13186

[17] Subramani Jegadeesan, Mohammad S. Obaidat, Pandi Vijayakumar, Maria Azees, Marimuthu Karuppiah: Efficient privacy-preserving anonymous authentication scheme for human predictive online education system. Clust. Comput. 25(4): 2557-2571 (2022) https://doi.org/10.1007/s10586-021-03390-5

[18] Anuj Garg, Sharmila A, Pramod Kumar, Mani Madhukar, Octavio Loyola-González, Manoj Kumar: Blockchain-based online education content ranking. Educ. Inf. Technol. 27(4): 4793-4815 (2022) https://doi.org/10.1007/s10639-021-10797-5