Towards automatic identification of core concepts in educational resources

机译：致力于自动识别教育资源中的核心概念

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摘要

Automatically identifying and extracting key ideas and concepts from educational resources is an important but challenging computational task. We present a supervised machine learning approach to assessing the “coreness” of concepts expressed by resource sentences. The algorithm has been developed and evaluated in the domain of science education where coreness refers to the degree to which a sentence embodies key concepts important to developing a robust understanding of the domain. Our method operates by automatically computing and leveraging the degree of semantic similarity between resource sentences and standard domain concepts designed by human experts for various STEM domains. In our experiments, the algorithm demonstrates high accuracy in identifying sentence coreness when there is agreement between human experts on the coreness rating. We also present performance comparisons with a number of baseline systems.

机译：从教育资源中自动识别和提取关键思想和观念是一项重要但具有挑战性的计算任务。我们提出了一种有监督的机器学习方法，以评估资源句子表达的概念的“核心”。该算法已在科学教育领域得到开发和评估，其中核心性是指句子体现关键概念的程度，这些关键概念对于发展对该领域的强大理解至关重要。我们的方法通过自动计算并利用资源语句与人类专家为各种STEM领域设计的标准领域概念之间的语义相似度进行操作。在我们的实验中，当人类专家之间对句子核心度达成一致时，该算法证明了在识别句子核心度方面的高精度。我们还介绍了与许多基准系统的性能比较。

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来源
《2014 IEEE/ACM Joint Conference on Digital Libraries》|2014年|379-388|共10页
会议地点 London(GB)
作者
Sultan M.A.; Bethard S.; Sumner T.;
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作者单位

Dept. of Comput. Sci., Univ. of Colorado Boulder, Boulder, CO, USA;

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原文格式 PDF
正文语种 eng
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关键词
educational computing; learning (artificial intelligence); text analysis; STEM domains; baseline systems; computational task; core concepts; educational resources; resource sentences; science education; semantic similarity; sentence coreness; supervised machine learning; Computational modeling; Feature extraction; Libraries; Semantics; Standards; Training; Core concepts; Semantic similarity; Text summarization;

机译：教育计算;学习（人工智能）;文本分析; STEM域;基线系统;计算任务;核心概念;教育资源;资源句;科学教育;语义相似性;句子核心;监督机器学习;计算建模;特征提取;图书馆语义;标准;培训;核心概念;语义相似度;文本概述;;

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1. Towards automatic identification of core concepts in educational resources [J] . B. Hazeltine Computing reviews . 2015,第8期

机译：致力于自动识别教育资源中的核心概念
2. Extracting learning concepts from educational texts in intelligent tutoring systems automatically [J] . Korhan Guenel, Rifat Asliyan Expert systems with applications . 2010,第7期

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3. Water Resources Allocation Using Solution Concepts of Fuzzy Cooperative Games: Fuzzy Least Core and Fuzzy Weak Least Core [J] . Mojtaba Sadegh, Reza Kerachian Water Resources Management . 2011,第10期

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4. Towards automatic identification of core concepts in educational resources [C] . Sultan M.A., Bethard S., Sumner T. IEEE/ACM Joint Conference on Digital Libraries . 2014

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5. Machine Learning Methods for Building Educational Applications: Concept Prerequisite Learning and Automatic Distractor Generation [D] . Liang, Chen 2018

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6. Automatic identification of pneumonia related concepts on chest x-ray reports. [O] . M. Fiszman, W. W. Chapman, S. R. Evans, 1999

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Towards automatic identification of core concepts in educational resources

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