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Linkify: A Web-Based Collaborative Content Tagging System for Machine Learning Algorithms

机译：Linkify：基于Web的机器学习算法协作内容标记系统

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摘要

Automated tutoring systems that use machine learning algorithms are a relatively new development which promises to revolutionize education by providing students on a large scale with an experience that closely resembles one-on-one tutoring. Machine learning algorithms are essential for these systems, as they are able to perform, with fairly good results, certain data processing tasks that have usually been considered difficult for artificial intelligence. However, the high performance of several machine learning algorithms relies on the existence of information about what is being processed in the form of tags, which have to be manually added to the content. Therefore, there is a strong need today for tagged educational resources. Unfortunately, tagging can be a very time-consuming task. Proven strategies for the mass tagging of content already exist: collaborative tagging systems, such as Delicious, StumbleUpon and CiteULike, have been growing in popularity in recent years. These websites allow users to tag content and browse previously tagged content that is relevant to the user's interests.;However, attempting to apply this particular strategy towards educational resource tagging presents several problems. Tags for educational resources to be used in tutoring systems need to be highly accurate, as mistakes in recommending or assigning material to students can be very detrimental to their learning, so ideally subject-matter experts would perform the resource tagging. The issue with hiring experts is that they can sometimes be not only scarce but also expensive, therefore limiting the number of resources that could potentially be tagged. Even if non-experts are used, another issue arises from the fact that a large user base would be required to tag large amounts of resources, and acquiring large numbers of users can be a challenge in itself.;To solve these problems, we present Linkify, a system that allows the more accurate tagging of large amounts of educational resources by combining the efforts of users with certain existing machine learning algorithms that are also capable of tagging resources. This thesis will discuss Linkify in detail, presenting its database structure and components, and discussing the design choices made during its development. We will also discuss a novel model for tagging errors based on a binary asymmetric channel. From this model, we derive an EM algorithm which can be used to combine tags entered into the Linkify system by multiple users and machine learning algorithms, producing the most likely set of relevant tags for each given educational resource. Our goal is to enable automated tutoring systems to use this tagging information in the future in order to improve their capability of assessing student knowledge and predicting student performance. At the same time, Linkify's standardized structure for data input and output will facilitate the development and testing of new machine learning algorithms.
机译：使用机器学习算法的自动补习系统是一个相对较新的发展，它有望通过为大规模学生提供与一对一补习非常相似的体验来彻底改变教育。机器学习算法对于这些系统至关重要，因为它们能够以良好的结果执行通常被认为对人工智能来说很困难的某些数据处理任务。但是，几种机器学习算法的高性能取决于是否存在以标签形式处理的信息，这些信息必须手动添加到内容中。因此，当今强烈需要带标签的教育资源。不幸的是，标记可能是非常耗时的任务。大规模标记内容的行之有效的策略已经存在：协作标记系统，例如Delicious，StumbleUpon和CiteULike，近年来已经越来越流行。这些网站允许用户标记内容并浏览与用户兴趣相关的先前标记的内容。但是，尝试将这种特定策略应用于教育资源标记存在一些问题。辅导系统中使用的教育资源标签必须非常准确，因为向学生推荐或分配材料的错误可能对他们的学习非常不利，因此理想情况下，主题专家将对资源进行标签。招聘专家的问题是，有时他们有时不仅稀缺而且昂贵，因此限制了可能被标记的资源数量。即使使用非专家，另一个问题是由于需要大量的用户基础来标记大量资源，而获取大量用户本身可能是一个挑战。为了解决这些问题，我们提出Linkify，一种系统，通过将用户的努力与某些现有的能够标记资源的机器学习算法结合在一起，可以更准确地标记大量的教育资源。本文将详细讨论Linkify，介绍其数据库结构和组件，并讨论其开发过程中所做的设计选择。我们还将讨论一种基于二进制非对称通道标记错误的新颖模型。从该模型中，我们得出了一种EM算法，该算法可用于组合多个用户输入到Linkify系统中的标签和机器学习算法，从而为每个给定的教育资源生成最可能的相关标签集。我们的目标是使自动化辅导系统将来能够使用此标记信息，以提高其评估学生知识和预测学生表现的能力。同时，Linkify的数据输入和输出标准化结构将促进新机器学习算法的开发和测试。

著录项

  • 作者

    Soares, Dante.;

  • 作者单位

    Rice University.;

  • 授予单位 Rice University.;
  • 学科 Electrical engineering.;Artificial intelligence.;Educational technology.
  • 学位 M.S.
  • 年度 2014
  • 页码 80 p.
  • 总页数 80
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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