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Research paper | Open Access

Application of keyword extraction on MOOC resources

Zhuoxuan Jiang1( ), Chunyan Miao2, Xiaoming Li1
Peking University, Beijing, China
Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University, Singapore, Singapore and School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
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Abstract

Purpose

Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well.

Design/methodology/approach

Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels.

Findings

From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform.

Research limitations/implications

Conducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required.

Practical implications

The experiment results support that MOOC resources are good enough for building models of keyword extraction, and an acceptable balance between human efforts and model accuracy can be achieved.

Originality/value

This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings.

Keywords

Concept mapGraph modelKeyword extractionLearning pathMassive Open Online Courses (MOOCs)

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International Journal of Crowd Science
Volume 1 Issue 1,
March 2017
Pages 48-70
DOI: 10.1108/IJCS-12-2016-0003
Cite this article:
Jiang Z, Miao C, Li X. Application of keyword extraction on MOOC resources. International Journal of Crowd Science, 2017, 1(1): 48-70. https://doi.org/10.1108/IJCS-12-2016-0003

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Received: 02 December 2016
Revised: 17 January 2017
Accepted: 19 January 2017
Published: 06 March 2017
© The author(s)

Zhuoxuan Jiang, Chunyan Miao and Xiaoming Li. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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