基于数据挖掘的CSCL用户角色发现策略

Jian Liao, Yanyan Li, Pen Chen, Ronghuai Huang
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引用次数: 8

摘要

本文的目的是展示数据挖掘如何作为一种发现CSCL学习者角色的策略提供希望。目前,有许多研究者关注基于协作学习活动的学习者角色分析。但大多是事先主观假设学习者角色的多样性,然后根据交互数据的统计进行验证。相反,本文采用数据挖掘技术来探索学习者在协作学习中的角色。参考数据挖掘的典型过程,提出了一个基于数据挖掘的角色分析框架,描述了数据准备和学习者话语模式挖掘。最后,通过实例分析说明了采矿过程和发现,并对采矿结果进行了讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Data Mining as a Strategy for Discovering User Roles in CSCL
The purpose of this paper is to show how data mining may offer promise as a strategy for discovering learner's roles in CSCL. At present, there are many researchers who focus on analyzing learner roles based on collaborative learning activities. But most of them subjectively presume the diversity of learnerspsila roles in advance and then verify it based on the statistics of interaction data. In contrast, this paper adopts the data mining technology to explore learnerspsila roles in collaborative learning. Referring to the typical process of data mining, this paper proposes a framework of role analysis based on data mining which data preparation and learner discourse pattern mining are depicted. Furthermore, a case study is conducted to show the mining process and finding as well as discussion on the mining results.
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