Automatic Generation of Multiple Choice Questions from Slide Content using Linked Data

Assessment plays an important role in the process of learning. Multiple choice questions (MCQs) are suitable candidates to fulfill this role. We present an approach for automatically generating MCQs based on the content presented in slides. It extracts named entities from slides, and queries a knowledge base to create different varieties of MCQs and appropriate answer options. Users can choose between different levels of difficulty for the generated questions and answer options. The approach can be easily extended to generate other varieties of MCQs. Results from a user study confirm the applicability and appropriateness of the approach.

[1]  Axel-Cyrille Ngonga Ngomo,et al.  ASSESS - Automatic Self-Assessment Using Linked Data , 2015, SEMWEB.

[2]  Manish Agarwal,et al.  Automatic Cloze-Questions Generation , 2013, RANLP.

[3]  Johannes Hoffart,et al.  YAGO2s: Modular High-Quality Information Extraction with an Application to Flight Planning , 2013, BTW.

[4]  Bijan Parsia,et al.  An Experimental Evaluation of Automatically Generated Multiple Choice Questions from Ontologies , 2016, OWLED.

[5]  Hiroshi Nakagawa,et al.  A Real-Time Multiple-Choice Question Generation For Language Testing: A Preliminary Study , 2005 .

[6]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[7]  Lee Becker,et al.  Mind the Gap: Learning to Choose Gaps for Question Generation , 2012, HLT-NAACL.

[8]  Christian Bizer,et al.  DBpedia spotlight: shedding light on the web of documents , 2011, I-Semantics '11.

[9]  Nelia Lasierra,et al.  LinkSUM: Using Link Analysis to Summarize Entity Data , 2016, ICWE.

[10]  Vinay K. Chaudhri,et al.  Question Generation from a Knowledge Base , 2014, EKAW.

[11]  Tsukasa Hirashima,et al.  Automated Question Generation Methods for Intelligent English Learning Systems and its Evaluation , 2001 .

[12]  Ming Liu,et al.  Automatic Question Generation for Literature Review Writing Support , 2010, Intelligent Tutoring Systems.

[13]  Andreas Papasalouros,et al.  Automatic Generation Of Multiple Choice Questions From Domain Ontologies , 2008, e-Learning.

[14]  Achim Rettinger,et al.  PageRank on Wikipedia: Towards General Importance Scores for Entities , 2016, @ESWC.

[15]  Manish Agarwal,et al.  Automatic Gap-fill Question Generation from Text Books , 2011, BEA@ACL.

[16]  Adam Kilgarriff,et al.  Gap-fill Tests for Language Learners: Corpus-Driven Item Generation , 2010 .

[17]  Sadid A. Hasan,et al.  Automatic Question Generation from Sentences , 2010, JEPTALNRECITAL.

[18]  Maha M. Al-Yahya,et al.  OntoQue: A Question Generation Engine for Educational Assesment Based on Domain Ontologies , 2011, 2011 IEEE 11th International Conference on Advanced Learning Technologies.

[19]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[20]  Mohamed Yahya,et al.  Knowledge Questions from Knowledge Graphs , 2016, ICTIR.

[21]  Maxine Eskénazi,et al.  Automatic Question Generation for Vocabulary Assessment , 2005, HLT.

[22]  R. Mitkov,et al.  Computer-Aided Generation of Multiple-Choice Tests , 2003, International Conference on Natural Language Processing and Knowledge Engineering, 2003. Proceedings. 2003.

[23]  Rafael E. Banchs,et al.  RevUP: Automatic Gap-Fill Question Generation from Educational Texts , 2015, BEA@NAACL-HLT.

[24]  David Lennart Lindberg.  Automatic question generation from text for self-directed learning , 2013 .