Ontology-Based Multiple Choice Question Generation

In this chapter, an ontology that structures all the cell organelles and their parts are modelled to cognitively model domain knowledge by explicitly establishing relationships among them. The ontologies are modelled depicting the cell as a system and the parts of the cell as the subclasses of the cell along with various functionalities and behavior. The model further focuses on education pedagogy to generate questions based on the modelled ontologies. Furthermore, the defined ontologies are made consistent by defining the classes and the relationship between them, initializing the instances and axiomatizing the developed ontological content. The modelled ontologies are semiotically evaluated using various learners and domain experts. An overall reuse ratio of 0.91 has been achieved, and the proposed ontology has been differentiated from the existing cell ontologies by focusing on an educational pedagogy. Ultimately, an ontology-focused algorithm for multiple choice question generation has been proposed for cell biology as a domain of choice with an accuracy of 90.03%.

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A Novel Student-Led Approach to Multiple-Choice Question Generation and Online Database Creation, With Targeted Clinician Input

Teaching and Learning in Medicine ◽

10.1080/10401334.2015.1011651 ◽

2015 ◽

Vol 27 (2) ◽

pp. 182-188 ◽

Cited By ~ 9

Author(s):

Benjamin H. L. Harris ◽

Jason L. Walsh ◽

Saadia Tayyaba ◽

David A. Harris ◽

David J. Wilson ◽

...

Keyword(s):

Multiple Choice ◽

Multiple Choice Question ◽

Online Database ◽

Question Generation

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An Empirical Analysis of Online Multiple-Choice Question-Generation Learning Activity for the Enhancement of Students’ Cognitive Strategy Development While Learning Science

Recent Progress in Computational Sciences and Engineering ◽

10.1201/9780429070655-139 ◽

2019 ◽

pp. 585-588

Author(s):

F.Y. Yu ◽

C.C. Hung

Keyword(s):

Empirical Analysis ◽

Multiple Choice ◽

Cognitive Strategy ◽

Multiple Choice Question ◽

Strategy Development ◽

Learning Activity ◽

Learning Science ◽

Question Generation

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An Automated Multiple-Choice Question Generation using Natural Language Processing Techniques

International Journal on Natural Language Computing ◽

10.5121/ijnlc.2021.10201 ◽

2021 ◽

Vol 10 (02) ◽

pp. 1-10

Author(s):

Chidinma A. Nwafor ◽

Ikechukwu E. Onyenwe

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Multiple Choice ◽

Automatic Generation ◽

Multiple Choice Question ◽

Question Generation ◽

Computer Based ◽

Processing Techniques ◽

User Friendly

Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are important words in a given lesson material. To validate that the system is not perverse, five lesson materials were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the teacher were compared to the auto-generated keywords and the result shows that the system was capable of extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a user-friendly interface for easy accessibility.

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