Structuring lectures in massive open online courses (MOOCs) for efficient learning by linking similar sections and predicting prerequisites

Shen, Sheng-syun; Lee, Hung-yi; Li, Shang-Wen; Zue, Victor W.; Lee, Lin-Shan

doi:10.21437/interspeech.2015-49

Cited by 5 publications

(2 citation statements)

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“…The identification of d2d relationships has been performed in different scenarios. For example, the work described in [5] finds d2d relationships with the goal of structuring lectures from Massive Open Online Courses (MOOCs) into a graph. The motivation is that the way of searching in MOOC platforms is to enter a query and examine the retrieved list of courses.…”

Section: Related Workmentioning

confidence: 99%

“…By using ts2ts relationships it is possible to have immediate access to content on the same topic in different documents. Current research work has given proof that structuring documents based on d2d relationships brings both browsing efficiency and understanding of the topics in the documents [4,5]. Therefore, we believe that similar advantages should also be brought to document topic segments.…”

Section: Introductionmentioning

confidence: 99%

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Graph-Community Detection for Cross-Document Topic Segment Relationship Identification

Mota,

Eskenazi,

Coheur

2016

Preprint

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In this paper we propose a graph-community detection approach to identify cross-document relationships at the topic segment level. Given a set of related documents, we automatically find these relationships by clustering segments with similar content (topics). In this context, we study how different weighting mechanisms influence the discovery of word communities that relate to the different topics found in the documents. Finally, we test different mapping functions to assign topic segments to word communities, determining which topic segments are considered equivalent.By performing this task it is possible to enable efficient multi-document browsing, since when a user finds relevant content in one document we can provide access to similar topics in other documents. We deploy our approach in two different scenarios. One is an educational scenario where equivalence relationships between learning materials need to be found. The other consists of a series of dialogs in a social context where students discuss commonplace topics. Results show that our proposed approach better discovered equivalence relationships in learning material documents and obtained close results in the social speech domain, where the best performing approach was a clustering technique.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%