TEDQuiz: Automatic Quiz Generation for TED Talks Video Clips to Assess Listening Comprehension

In the last few years, researchers in the field of e-learning and Natural Language Processing (NLP) have shown an increased interest in automatic question generation. However, little research has discussed the automatic evaluation of listening comprehension in multimedia learning. In this work, we p...

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Bibliographic Details
Published in:2014 IEEE 14th International Conference on Advanced Learning Technologies pp. 350 - 354
Main Authors: Yi-Ting Huang, Ya-Min Tseng, Sun, Yeali S., Meng Chang Chen
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2014
Subjects:
computer assisted language learning
Computers
Conferences
Context
Materials
Multimedia communication
multimedia learning
Natural language processing
question generation
Streaming media
ISSN:2161-3761
Online Access:Get full text
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Summary:In the last few years, researchers in the field of e-learning and Natural Language Processing (NLP) have shown an increased interest in automatic question generation. However, little research has discussed the automatic evaluation of listening comprehension in multimedia learning. In this work, we present an automatic quiz generation for TED Talks video clips, called TED Quiz. TED Quiz generates multiple-choice questions in two question types, gist-content questions and detail questions. We use a graph-based algorithm, Lex Rank, to identify the most important part of a talk, as the main concept of a gist-content question. We also proposed an approach to distractor selection for detail question generation that generates grammatically correct but semantically wrong sentences as distractors. The experimental results demonstrated that the measured results from automatically generated questions are comparable with that from manually generated questions because their scores were significantly correlated. Moreover, most subjects agreed that the generated listening comprehension questions were of quality and usefulness.
ISSN:2161-3761
DOI:10.1109/ICALT.2014.105