In the context of globalization, the trend of globalization in the field of education is becoming more and more significant. With the maturity of Internet and cloud computing, a large online open course learning platform providing courses and educational services for global users has emerged in the past few years. This is not only the innovation of Internet applications, but it is also believed that it will trigger a change in higher education and social development. The main body of online open courses is learners, and its biggest feature is that there are a large number of learners and a variety of learner groups. Due to the characteristics of Internet technology, all learning behaviors of learners on the online open course platform will be recorded in the form of rich and diverse data. Therefore, it is necessary to analyze learners’ learning behavior. This paper proposes a dual-channel clustering algorithm, which analyzes and mines a large number of learning behavior data of more than 5000 learners in online open courses of a university. This method takes fine-grained data as the core, obtains the types of learners in different models through dual-channel clustering calculation, and finally characterizes learners based on the fused model. Compared with three state-of-the-art clustering algorithms, the experimental results show that the proposed dual-channel clustering algorithm can enhance the cohesion of clusters, cluster learners more accurately, and characterize learners’ profiles more deeply and comprehensively.
References
[1]
Cheon, J., Cheng, J. M., & Cho, M. H. (2021). Validation of the Online Learning Readiness Self-Check Survey. Distance Education, 42, 599-619.
https://doi.org/10.1080/01587919.2021.1986370
[2]
Fan, J., Jiang, Y., Liu, Y., & Zhou, Y. (2021). Interpretable MOOC Recommendation: A Multi-Attention Network for Personalized Learning Behavior Analysis. Internet Research, 32, 588-605. https://doi.org/10.1108/INTR-08-2020-0477
[3]
Fard, E. R., Aghayar, K., & Amniat-Talab, M. (2018). Quantum Pattern Recognition with Multi-Neuron Interactions. Quantum Information Processing, 17, Article No. 42.
https://doi.org/10.1007/s11128-018-1816-y
[4]
Giddens, J., Curry-Lourenco, K., Miles, E., & Reeder E. (2021). Enhancing Learning in an Online Doctoral Course through a Virtual Community Platform. Journal of Professional Nursing, 37, 184-189. https://doi.org/10.1016/j.profnurs.2020.05.007
[5]
Heymann, P., Bastiaens, E., Jansen, A., van Rosmalen, P., & Beausaert, S. (2022). A Conceptual Model of Students’ Reflective Practice for the Development of Employability Competences, Supported by an Online Learning Platform. Education and Training. (In Print) https://doi.org/10.1108/ET-05-2021-0161
[6]
Hoi, S. C. H., Sahoo, D., Lu, J., & Zhao, P. (2021). Online Learning: A Comprehensive Survey. Neurocomputing, 459, 249-289. https://doi.org/10.1016/j.neucom.2021.04.112
[7]
Jiao, G., & Li, W. (2021). Neural Network Data Mining Clustering Optimization Algorithm. IETE Journal of Research. https://doi.org/10.1080/03772063.2021.1965043
[8]
Jin, C. (2020). MOOC Student Dropout Prediction Model Based on Learning Behavior Features and Parameter Optimization. Interactive Learning Environments.
Kausar, S., Xu, H., Hussain, I., Zhu, W., & Zahid, M. (2018). Integration of Data Mining Clustering Approach in the Personalized E-Learning System. IEEE Access, 6, 72724-72734.
https://doi.org/10.1109/ACCESS.2018.2882240
[11]
Khan, Z., Yang, J., & Zheng, Y. (2019). Efficient Clustering Approach for Adaptive Unsupervised Colour Image Segmentation. IET Image Processing, 13, 1763-1772.
https://doi.org/10.1049/iet-ipr.2018.5976
[12]
Kuo, T. M., Tsai, C. C., & Wang, J. C. (2021). Linking Web-Based Learning Self-Efficacy and Learning Engagement in MOOCs: The Role of Online Academic Hardiness. Internet and Higher Education, 51, Article ID: 100819.
https://doi.org/10.1016/j.iheduc.2021.100819
[13]
Lei, X., & Ouyang, H. (2019). Image Segmentation Algorithm Based on Improved Fuzzy Clustering. Cluster Computing, 22, 13911-13921.
https://doi.org/10.1007/s10586-018-2128-9
[14]
Liu, Y., & Li, B. (2020). Bayesian Hierarchical K-Means Clustering. Intelligent Data Analysis, 24, 977-992. https://doi.org/10.3233/IDA-194807
[15]
Lv, J., Wang, X., Ren, K., Huang, M., & Li, K. (2017). ACO-Inspired Information-Centric Networking Routing Mechanism. Computer Networks, 126, 200-217.
https://doi.org/10.1016/j.comnet.2017.07.004
[16]
Ma, L., Huang, M., Yang, S., Wang, R., & Wang, X. (2021). An Adaptive Localized Decision Variable Analysis Approach to Large-Scale Multiobjective and Many-Objective Optimization. IEEE Transactions on Cybernetics, 52, 1-13.
https://doi.org/10.1109/TCYB.2020.3041212
[17]
Riaz, M., Habib, A., Khan, M. J., & Kumam, P. (2021). Correlation Coefficients for Cubic Bipolar Fuzzy Sets with Applications to Pattern Recognition and Clustering Analysis. IEEE Access, 9, 109053-109066. https://doi.org/10.1109/ACCESS.2021.3098504
[18]
Sedrakyan, G., Malmberg, J., Verbert, K., Jarvela, S., & Kirschner, P. A. (2020). Linking Learning Behavior Analytics and Learning Science Concepts: Designing a Learning Analytics Dashboard for Feedback to Support Learning Regulation. Computers in Human Behavior, 107, Article ID: 105512. https://doi.org/10.1016/j.chb.2018.05.004
[19]
Shou, Z., Lu, X., Wu, Z., Yuan, H., Zhang, H., & Lai, J. (2020). On Learning Path Planning Algorithm Based on Collaborative Analysis of Learning Behavior. IEEE Access, 8, 119863-119879. https://doi.org/10.1109/ACCESS.2020.3005793
[20]
Sinaga, K. P., & Yang, M. S. (2020). Unsupervised K-Means Clustering Algorithm. IEEE Access, 8, 80716-80727. https://doi.org/10.1109/ACCESS.2020.2988796
[21]
Son, S., & Hong, S. (2021). Multiple Group Analysis in Multilevel Data across Within-Level Groups: A Comparison of Multilevel Factor Mixture Modeling and Multilevel Multiple-Indicators Multiple-Causes Modeling. Educational and Psychological Measurement, 81, 904-935. https://doi.org/10.1177/0013164420987899
[22]
Starczewski, A., Scherer, M. M., Ksiazek, W., Debski, M., & Wang, L. (2021). A Novel Grid-Based Clustering Algorithm. Journal of Artificial Intelligence and Soft Computing Research, 11, 319-330. https://doi.org/10.2478/jaiscr-2021-0019
[23]
Sun, Z., Anbarasan, M., & Kumar, D. P. (2020). Design of Online Intelligent English Teaching Platform Based on Artificial Intelligence Techniques. Computational Intelligence, 37, 1166-1180. https://doi.org/10.1111/coin.12351
[24]
Tan, D., Zhong, W., Jiang, C., Peng, X., & He, W. (2020). High-Order Fuzzy Clustering Algorithm Based on Multikernel Mean Shift. Neurocomputing, 385, 63-79.
https://doi.org/10.1016/j.neucom.2019.12.030
[25]
Trakunphutthirak, R., & Lee, V. C. S. (2021). Application of Educational Data Mining Approach for Student Academic Performance Prediction Using Progressive Temporal Data. Journal of Educational Computing Research.
https://doi.org/10.1177/07356331211048777
[26]
Wang, J., Wang, X., Yu, G., Domeniconi, C., Yu, Z., & Zhang, Z. (2021). Discovering Multiple Co-Clusterings with Matrix Factorization. IEEE Transactions on Cybernetics, 51, 3576-3587. https://doi.org/10.1109/TCYB.2019.2950568
Wang, S. (2021). Online Learning Behavior Analysis Based on Image Emotion Recognition. Traitement Du Signal, 38, 865-873. https://doi.org/10.18280/ts.380333
[29]
Wang, W., Guo, L., & Sun, R. (2019). Rational Herd Behavior in Online Learning: Insights from MOOC. Computers in Human Behavior, 92, 660-669.
https://doi.org/10.1016/j.chb.2017.10.009
[30]
Xu, Q., Zhang, Q., Liu, J., & Luo, B. (2020). Efficient Synthetical Clustering Validity Indexes for Hierarchical Clustering. Expert Systems with Applications, 151, Article ID: 113367. https://doi.org/10.1016/j.eswa.2020.113367
[31]
Xu, X., Wang, J., Peng, H., & Wu, R. (2019). Prediction of Academic Performance Associated with Internet Usage Behaviors Using Machine Learning Algorithms. Computers in Human Behavior, 98, 166-173. https://doi.org/10.1016/j.chb.2019.04.015
[32]
Xue, Y., Qin, J., Su, S., & Slowik, A. (2021). Brain Storm Optimization Based Clustering for Learning Behavior Analysis. Computer Systems and Engineering, 39, 211-219.
https://doi.org/10.32604/csse.2021.016693
[33]
Yang, M. S., Chang-Chien, S. J., & Nataliani, Y. (2019). Unsupervised Fuzzy Model-Based Gaussian Clustering. Information Sciences, 481, 1-23.
https://doi.org/10.1016/j.ins.2018.12.059
[34]
Yang, T. C., & Chen, S. Y. (2020). Investigating Students’ Online Learning Behavior with a Learning Analytic Approach: Field Dependence/Independence VS. Holism/Serialism. Interactive Learning Environments. https://doi.org/10.1080/10494820.2020.1817759
[35]
Zhang, J., Zuo, L., Miao, J., Zhang, Y., Hwang, G., & Zhu, Y. (2020). An Individualized Intervention Approach to Improving University Students’ Learning Performance and Interactive Behaviors in a Blended Learning Environment. Interactive Learning Environments, 28, 231-245. https://doi.org/10.1080/10494820.2019.1636078
[36]
Zhao, F., Hwang, G., & Yin, C. (2021). A Result Confirmation-Based Learning Behavior Analysis Framework for Exploring the Hidden Reasons behind Patterns and Strategies. Educational Technology & Society, 24, 138-151.
[37]
Zou, H. (2020). Clustering Algorithm and Its Application in Data Mining. Wireless Personal Communications, 110, 21-30. https://doi.org/10.1007/s11277-019-06709-z
