{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T22:42:16Z","timestamp":1748472136469},"reference-count":38,"publisher":"Tsinghua University Press","issue":"1","license":[{"start":{"date-parts":[[2017,3,6]],"date-time":"2017-03-06T00:00:00Z","timestamp":1488758400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCS"],"published-print":{"date-parts":[[2017,3,6]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijcs-12-2016-0003","type":"journal-article","created":{"date-parts":[[2017,5,17]],"date-time":"2017-05-17T07:27:42Z","timestamp":1495006062000},"page":"48-70","source":"Crossref","is-referenced-by-count":1,"title":["Application of keyword extraction on MOOC resources"],"prefix":"10.26599","volume":"1","author":[{"given":"Zhuoxuan","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyan","family":"Miao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"11138","reference":[{"key":"ref38","first-page":"130","article-title":"Sentiment analysis in MOOC discussion forums: what does it tell us?","author":"wen","year":"0","journal-title":"EDM'14"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.5120\/16899-6972"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2010.2053532"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/1031171.1031181"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3115\/1596374.1596399"},{"key":"ref37","first-page":"226","article-title":"Investigating how student's cognitive behavior in MOOC discussion forums affect learning gains","author":"wang","year":"2015","journal-title":"The 8th International Conference on Educational Data Mining EDM'15"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2005.11.020"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3115\/1073445.1073478"},{"key":"ref34","first-page":"267","article-title":"An introduction to conditional random fields","volume":"4","author":"sutton","year":"2011","journal-title":"Machine Learning"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s007999900023"},{"key":"ref11","article-title":"HarvardX and MITx: the first year of open online courses","author":"ho","year":"2013","journal-title":"HarvardX and MITx Working Paper No 1"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICSMC.2006.385173"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2556325.2566249"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICALT.2015.149"},{"key":"ref15","first-page":"242","article-title":"Influence analysis by heterogeneous network in MOOC forums: what can we discover?","author":"jiang","year":"0"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1017\/S1351324900000048"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2460296.2460330"},{"key":"ref18","first-page":"282","article-title":"Conditional random fields: probabilistic models for segmenting and labeling sequence data","author":"lafferty","year":"0","journal-title":"Proceedings of the 18th International Conference on Machine Learning 2001 ICML'01"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.137"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-7552(98)00110-X"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1177\/0165551513494645"},{"key":"ref3","first-page":"13","article-title":"Studying learning in the worldwide classroom: Research into edX's first MOOC","volume":"8","author":"breslow","year":"2013","journal-title":"Research & Practice in Assessment"},{"key":"ref27","article-title":"The theory underlying concept maps and how to construct and use them","author":"novak","year":"2006","journal-title":"Technical Report IHMC CmapTools 2006-01 Rev 2008-01"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2006.10.001"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3115\/1626394.1626430"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37247-6_12"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623623"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICALT.2007.7"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23226-8_25"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3115\/1219840.1219885"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2566486.2568042"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.11.049"},{"key":"ref22","first-page":"171","article-title":"Automatic concept relationships discovery for an adaptive e-course","author":"marian","year":"2009","journal-title":"Proc of the 2nd International Conference on Data Mining"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5131-9"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1075\/li.30.1.03nad"},{"key":"ref23","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","author":"mikolov","year":"2013","journal-title":"NIPS'13 Proceedings of the 26th International Conference on Neural Information Processing Systems"},{"key":"ref26","first-page":"49","article-title":"Software document terminology recognition","author":"nojiri","year":"0","journal-title":"AAAI Spring Symposium"},{"key":"ref25","author":"nickel","year":"2015","journal-title":"A review of relational machine learning for knowledge graphs"}],"container-title":["International Journal of Crowd Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJCS-12-2016-0003\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJCS-12-2016-0003\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T17:07:58Z","timestamp":1660842478000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJCS-12-2016-0003\/full\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,6]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,3,6]]}},"alternative-id":["10.1108\/IJCS-12-2016-0003"],"URL":"https:\/\/doi.org\/10.1108\/ijcs-12-2016-0003","relation":{},"ISSN":["2398-7294"],"issn-type":[{"value":"2398-7294","type":"print"}],"subject":[],"published":{"date-parts":[[2017,3,6]]}}}