Data Mining and Case-Based Reasoning for Distance Learning

Data Mining and Case-Based Reasoning for Distance Learning

Ruimin Shen, Peng Han, Fan Yang, Qiang Yang, Joshua Z. Huang
Copyright: © 2003 |Pages: 13
DOI: 10.4018/jdet.2003070104
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Abstract

Web-based learning enables more students to have access to the distance-learning environment, and provides students and teachers with unprecedented flexibility and convenience. However, the early experience of using this new learning means in China exposes a few problems. Among others, teachers accustomed to traditional teaching methods often find it difficult to put their courses online, and some students, especially the adult students, find themselves overloaded with too much information. In this paper, we present an open framework to solve these two problems. This framework allows students to interact with an automated question answering system to get their answers. It enables teachers to analyze students’ learning patterns and organize the web-based contents efficiently. The framework is intelligent due to the data mining and case-based reasoning features, and user-friendly because of its personalized services to both teachers and students.

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