基于K-means算法的在线学习用户画像研究

2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE) Pub Date : 2021-12-17 DOI:10.1109/ICECE54449.2021.9674507

Yan Wang, Qinglin Wu

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引用次数: 0

摘要

在线学习是学习者获取知识的重要途径。它有助于学习者灵活地选择时间和地点，提高学习的效率和自主性。在分析在线学习和用户画像现状的基础上，提出了在线学习用户画像的一般流程。通过在线课程的数据收集，选择在线学习者的五个维度对在线学习者进行聚类。实验结果表明，将课程学习者分为四类，聚类结果令人满意，有助于提高在线学习的效果。通过对在线学习群画像特点的深入分析，为提高在线学习效果提供参考依据。

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Research on Online Learning User Profile Based on K-means Algorithm

Online learning is an important way for learners to acquire knowledge. It helps learners to choose time and place flexibly and improve the efficiency and autonomy of learning. Based on the analysis of the current situation of online learning and user portrait, the general process of online learning user portrait is proposed. Through the data collection of an online course, five dimensions of online learners are selected to cluster the online learners. The experimental results show that the clustering results are satisfactory when the learners of the course are clustered into four categories, Help to improve the effect of online learning. Through the in-depth analysis of the characteristics of online learning group portrait, it provides a reference basis for improving the effect of online learning.

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2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE)

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