PolyAQG: An Automated Question Generation Framework Based on a Combination of Four Analytical Approaches

Tee Hean Tan; Zaharin Yusoff

doi:doi:10.11648/j.mcs.20220704.12

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PolyAQG: An Automated Question Generation Framework Based on a Combination of Four Analytical Approaches

Tee Hean Tan, Zaharin Yusoff

Published in Mathematics and Computer Science (Volume 7, Issue 4)

Received: 26 July 2022 Accepted: 11 August 2022 Published: 17 August 2022

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Abstract

Automated Question Generation (AQG) that automatically generates questions from text effectively reduces the time and effort of educators in preparing and setting assessments questions. Since the 1970s, AQG researchers have proposed and applied a vast range of approaches to generate questions. With that, the PolyAQG Framework is proposed in this paper by combining four analytical AQG approaches into a framework to increase the number, the variety, and the quality of questions. The novelties offered by this PolyAQG Framework are highlighted in this paper. A prototype is developed under JavaFX platform and integrated with the Stanford NLP parser. From the 300 test sentences, the framework has successfully generated ten-times more questions with more than 85% of the generated questions were rated accurate and relevant. This test result revealed that the framework did successfully meet the three research objectives formulated for this study, as follows: First, a framework is developed by combining four analytical AQG techniques. Second, two phases are used to generate new sentences from the input and earlier phases are re-used to develop a new set of questions. Lastly, the generation of ontology-based questions phase does not merely generate distractors (typical in other ontology-based AQGs), but instead generates new sentences with ontology knowledge and uses them to create a new set of questions.

Published in	Mathematics and Computer Science (Volume 7, Issue 4)
DOI	10.11648/j.mcs.20220704.12
Page(s)	68-74
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Automated Question Generation, Syntax-Based, Semantic-Based, Ontology-Based

References

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[14]	A. Fabbri, P. Ng, Z. Wang, R. Nallapati, and B. Xiang, “Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering,” pp. 4508–4513, 2020, DOI: 10.18653/v1/2020.acl-main.413.
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APA Style

Tee Hean Tan, Zaharin Yusoff. (2022). PolyAQG: An Automated Question Generation Framework Based on a Combination of Four Analytical Approaches. Mathematics and Computer Science, 7(4), 68-74. https://doi.org/10.11648/j.mcs.20220704.12

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ACS Style

Tee Hean Tan; Zaharin Yusoff. PolyAQG: An Automated Question Generation Framework Based on a Combination of Four Analytical Approaches. Math. Comput. Sci. 2022, 7(4), 68-74. doi: 10.11648/j.mcs.20220704.12

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AMA Style

Tee Hean Tan, Zaharin Yusoff. PolyAQG: An Automated Question Generation Framework Based on a Combination of Four Analytical Approaches. Math Comput Sci. 2022;7(4):68-74. doi: 10.11648/j.mcs.20220704.12

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@article{10.11648/j.mcs.20220704.12,
  author = {Tee Hean Tan and Zaharin Yusoff},
  title = {PolyAQG: An Automated Question Generation Framework Based on a Combination of Four Analytical Approaches},
  journal = {Mathematics and Computer Science},
  volume = {7},
  number = {4},
  pages = {68-74},
  doi = {10.11648/j.mcs.20220704.12},
  url = {https://doi.org/10.11648/j.mcs.20220704.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20220704.12},
  abstract = {Automated Question Generation (AQG) that automatically generates questions from text effectively reduces the time and effort of educators in preparing and setting assessments questions. Since the 1970s, AQG researchers have proposed and applied a vast range of approaches to generate questions. With that, the PolyAQG Framework is proposed in this paper by combining four analytical AQG approaches into a framework to increase the number, the variety, and the quality of questions. The novelties offered by this PolyAQG Framework are highlighted in this paper. A prototype is developed under JavaFX platform and integrated with the Stanford NLP parser. From the 300 test sentences, the framework has successfully generated ten-times more questions with more than 85% of the generated questions were rated accurate and relevant. This test result revealed that the framework did successfully meet the three research objectives formulated for this study, as follows: First, a framework is developed by combining four analytical AQG techniques. Second, two phases are used to generate new sentences from the input and earlier phases are re-used to develop a new set of questions. Lastly, the generation of ontology-based questions phase does not merely generate distractors (typical in other ontology-based AQGs), but instead generates new sentences with ontology knowledge and uses them to create a new set of questions.},
 year = {2022}
}

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TY  - JOUR
T1  - PolyAQG: An Automated Question Generation Framework Based on a Combination of Four Analytical Approaches
AU  - Tee Hean Tan
AU  - Zaharin Yusoff
Y1  - 2022/08/17
PY  - 2022
N1  - https://doi.org/10.11648/j.mcs.20220704.12
DO  - 10.11648/j.mcs.20220704.12
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 68
EP  - 74
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20220704.12
AB  - Automated Question Generation (AQG) that automatically generates questions from text effectively reduces the time and effort of educators in preparing and setting assessments questions. Since the 1970s, AQG researchers have proposed and applied a vast range of approaches to generate questions. With that, the PolyAQG Framework is proposed in this paper by combining four analytical AQG approaches into a framework to increase the number, the variety, and the quality of questions. The novelties offered by this PolyAQG Framework are highlighted in this paper. A prototype is developed under JavaFX platform and integrated with the Stanford NLP parser. From the 300 test sentences, the framework has successfully generated ten-times more questions with more than 85% of the generated questions were rated accurate and relevant. This test result revealed that the framework did successfully meet the three research objectives formulated for this study, as follows: First, a framework is developed by combining four analytical AQG techniques. Second, two phases are used to generate new sentences from the input and earlier phases are re-used to develop a new set of questions. Lastly, the generation of ontology-based questions phase does not merely generate distractors (typical in other ontology-based AQGs), but instead generates new sentences with ontology knowledge and uses them to create a new set of questions.
VL  - 7
IS  - 4
ER  -

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Author Information

Tee Hean Tan

Centre for American Education, Sunway University, Sunway City, Malaysia
Zaharin Yusoff

Institute for Research, Development and Innovation, International Medical University, Kuala Lumpur, Malaysia

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Plain Text BibTeX RIS

APA Style

Tee Hean Tan, Zaharin Yusoff. (2022). PolyAQG: An Automated Question Generation Framework Based on a Combination of Four Analytical Approaches. Mathematics and Computer Science, 7(4), 68-74. https://doi.org/10.11648/j.mcs.20220704.12

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ACS Style

Tee Hean Tan; Zaharin Yusoff. PolyAQG: An Automated Question Generation Framework Based on a Combination of Four Analytical Approaches. Math. Comput. Sci. 2022, 7(4), 68-74. doi: 10.11648/j.mcs.20220704.12

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AMA Style

Tee Hean Tan, Zaharin Yusoff. PolyAQG: An Automated Question Generation Framework Based on a Combination of Four Analytical Approaches. Math Comput Sci. 2022;7(4):68-74. doi: 10.11648/j.mcs.20220704.12

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@article{10.11648/j.mcs.20220704.12,
  author = {Tee Hean Tan and Zaharin Yusoff},
  title = {PolyAQG: An Automated Question Generation Framework Based on a Combination of Four Analytical Approaches},
  journal = {Mathematics and Computer Science},
  volume = {7},
  number = {4},
  pages = {68-74},
  doi = {10.11648/j.mcs.20220704.12},
  url = {https://doi.org/10.11648/j.mcs.20220704.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20220704.12},
  abstract = {Automated Question Generation (AQG) that automatically generates questions from text effectively reduces the time and effort of educators in preparing and setting assessments questions. Since the 1970s, AQG researchers have proposed and applied a vast range of approaches to generate questions. With that, the PolyAQG Framework is proposed in this paper by combining four analytical AQG approaches into a framework to increase the number, the variety, and the quality of questions. The novelties offered by this PolyAQG Framework are highlighted in this paper. A prototype is developed under JavaFX platform and integrated with the Stanford NLP parser. From the 300 test sentences, the framework has successfully generated ten-times more questions with more than 85% of the generated questions were rated accurate and relevant. This test result revealed that the framework did successfully meet the three research objectives formulated for this study, as follows: First, a framework is developed by combining four analytical AQG techniques. Second, two phases are used to generate new sentences from the input and earlier phases are re-used to develop a new set of questions. Lastly, the generation of ontology-based questions phase does not merely generate distractors (typical in other ontology-based AQGs), but instead generates new sentences with ontology knowledge and uses them to create a new set of questions.},
 year = {2022}
}

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TY  - JOUR
T1  - PolyAQG: An Automated Question Generation Framework Based on a Combination of Four Analytical Approaches
AU  - Tee Hean Tan
AU  - Zaharin Yusoff
Y1  - 2022/08/17
PY  - 2022
N1  - https://doi.org/10.11648/j.mcs.20220704.12
DO  - 10.11648/j.mcs.20220704.12
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 68
EP  - 74
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20220704.12
AB  - Automated Question Generation (AQG) that automatically generates questions from text effectively reduces the time and effort of educators in preparing and setting assessments questions. Since the 1970s, AQG researchers have proposed and applied a vast range of approaches to generate questions. With that, the PolyAQG Framework is proposed in this paper by combining four analytical AQG approaches into a framework to increase the number, the variety, and the quality of questions. The novelties offered by this PolyAQG Framework are highlighted in this paper. A prototype is developed under JavaFX platform and integrated with the Stanford NLP parser. From the 300 test sentences, the framework has successfully generated ten-times more questions with more than 85% of the generated questions were rated accurate and relevant. This test result revealed that the framework did successfully meet the three research objectives formulated for this study, as follows: First, a framework is developed by combining four analytical AQG techniques. Second, two phases are used to generate new sentences from the input and earlier phases are re-used to develop a new set of questions. Lastly, the generation of ontology-based questions phase does not merely generate distractors (typical in other ontology-based AQGs), but instead generates new sentences with ontology knowledge and uses them to create a new set of questions.
VL  - 7
IS  - 4
ER  -

Copy | Download