Leveraging Book Indexes for Automatic Extraction of Concepts in MOOCs
Leveraging Book Indexes for Automatic Extraction of Concepts in MOOCs
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DOI：
10.1145/3386527.3406749
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Assma Boughoula;Aidan San;Chengxiang Zhai
中科院分区：
文献类型：
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作者：
Assma Boughoula;Aidan San;Chengxiang Zhai
概念是任何学习模块中的基本元素，因此对于建模，总结和预览任何模块的内容都非常有用。从在线教育材料中自动提取主要概念可以实现许多有用的应用。在本文中，我们建议利用教科书及其书后索引作为训练数据来训练有监督的机器学习算法，用于从教育领域的文本数据中自动提取概念。我们通过在三本教科书上训练神经网络来评估这一想法，并将训练好的神经网络应用于从两个MOOC的演讲稿中提取概念。我们的研究结果表明，这一方向的进一步探索大有希望。
Concepts are basic elements in any learning module and are thus very useful for modeling, summarizing, and previewing the content of any module. Automatic extraction of the major concepts from online education materials enables many useful applications. In this paper, we propose to leverage textbooks and their back-of-the-book indexes as training data to train a supervised machine learning algorithm for automatic extraction of concepts from text data in the education domain. We evaluate this idea by training neural networks on three textbooks and applying the trained neural networks to extract concepts from the lecture transcripts of two MOOCs. Our results suggest great promise for further exploration of this direction.