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Development and Evaluation of a computer-aided instruction systen on the Internet

互联网计算机辅助教学系统的开发与评估

基本信息

批准号：
07680413
负责人：
MIZOGUCHI Fumio
金额：
$ 1.41万
依托单位：
Science University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1995
资助国家：
日本
起止时间：
1995 至 1997
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-07680413/
关键词：
CAI system Internet WWW browser Electrical syllabus Inductive Learning Automatic classification 電子ドキュメント情報フィルタリング World wide web 情報検索個人の嗜好ハイパーテキスト HTML CAI

项目摘要

Computers are becoming the vehicle for an increasing range of everyday activities. WWW browsers and electronic mail systems are typical examples of the fast-growing area of computing.This research focused on a computer-aided instruction systems based on this recent Internet technology. In particular, we developed an electrical syllabus for our students at the Science University of Tokyo.To deal with a large volume of WWW pages for the syllabus, we developed an intelligent search engine, SEIL (Search Engine using Inductive Learning) , which finds user-desirable WWW pages without keywords. SEIL works as the following steps.First, SEIL presents some WWW pages to users and gets their interests interactively. Second, using Inductive Learning, the keywords that a user seems to have interest are derived automatically. Third, SEIL retrieves WWW pages using these keywords like normal engines. These steps makes search engine to detect implicit user's interests. We conducted some experiments where a number of students used the electrical syllabus and SEIL, and their impression was analyzed using our inductive learning system. We reported on this result and provided an insight from this experiment towards practical use of the electrical syllabus based on inductive learning.

计算机正在成为越来越广泛的日常活动的工具。WWW浏览器和电子邮件系统是快速发展的计算领域的典型代表，本文研究的重点是基于这一最新的互联网技术的计算机辅助教学系统。特别是，我们为东京科学大学的学生开发了一个电子教学大纲。为了处理教学大纲中海量的WWW页面，我们开发了一个智能搜索引擎SEIL(Search Engine Using Inductive Learning)，它可以找到用户想要的没有关键字的WWW页面。SEIL的工作步骤如下：首先，SEIL向用户呈现一些WWW页面，并交互地获取他们的兴趣。其次，使用归纳学习，用户似乎感兴趣的关键字会自动派生出来。第三，像普通引擎一样，SEIL使用这些关键字来检索WWW页面。这些步骤使得搜索引擎能够发现隐含的用户兴趣。我们进行了一些实验，其中一些学生使用了电子教学大纲和SEIL，并使用我们的归纳学习系统分析了他们的印象。我们报告了这一结果，并从这一实验中为基于归纳学习的电子教学大纲的实际应用提供了一个见解。