不同模糊聚类模型在网络学习者行为评价中的比较研究
Evaluation of E-Learners Behaviour using Different Fuzzy Clustering Models: A Comparative Study
摘要：
本文介绍了一种对网络学习者行为的评价方法，该方法将反馈给网络学习系统的决策者。学习者的个人资料在评价过程中起着至关重要的作用，以提高电子学习过程的性能。这项工作的重点是聚类的电子学习者的基础上，他们的行为到特定的类别，代表学习者的个人资料。学习者的班级被命名为正规班、工人班、临时班、差班和缺席班。这项工作可能会回答如何让坏学生变成正常学生的问题。这项工作提出了使用不同的模糊聚类技术，如模糊c-均值和核模糊c-均值，以找到学习者的类别和预测他们的配置文件。本文介绍了数据描述，准备，特征选择，并使用不同的模糊聚类模型的实验设计的主要阶段。分析所获得的结果，并与这些学习者的现实世界的行为比较证明，有78%的匹配。模糊聚类比清晰聚类更能反映学习者的行为。通过对FCM和KFCM的比较，证明了KFCM在预测学习者行为方面优于FCM。
英文摘要：
This paper introduces an evaluation methodologies for the e-learners' behaviour that will be a feedback to the decision makers in e-learning system. Learner's profile plays a crucial role in the evaluation process to improve the e-learning process performance. The work focuses on the clustering of the e-learners based on their behaviour into specific categories that represent the learner's profiles. The learners' classes named as regular, workers, casual, bad, and absent. The work may answer the question of how to return bad students to be regular ones. The work presented the use of different fuzzy clustering techniques as fuzzy c-means and kernelized fuzzy c-means to find the learners' categories and predict their profiles. The paper presents the main phases as data description, preparation, features selection, and the experiments design using different fuzzy clustering models. Analysis of the obtained results and comparison with the real world behavior of those learners proved that there is a match with percentage of 78%. Fuzzy clustering reflects the learners' behavior more than crisp clustering. Comparison between FCM and KFCM proved that the KFCM is much better than FCM in predicting the learners' behaviour.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
