A FRAMEWORK FOR AUTOMATICALLY GENERATING QUESTIONS FOR TOPICS IN DISCRETE MATHEMATICS

ECU Author/Contributor (non-ECU co-authors, if there are any, appear on document)

Salar Houshvand (Creator)

Institution

East Carolina University (ECU )

Web Site: http://www.ecu.edu/lib/

Abstract: Automated question generation is critical for realizing personalized learning. Also, learning research shows that answering questions is a more effective method than rereading the textbook multiple times. However, creating different types of questions is intellectually challenging and time-intensive. Therefore, it emphasizes a necessity for an automated way to generate questions and evaluate them. In this research after analyzing the existing approaches to automated question generation, we conclude that most of the current systems use natural language process techniques to extract questions from the text, therefore, other topics such as mathematics are lacking an automated question generation system that could help learners to assess their knowledge.In this research we present a novel framework that automatically generates unlimited numbers of questions for different topics in discrete mathematics. We created multiple algorithms for various questions in four main topics using Python. Our final product is presented as an application programming interface (API) using Flask library, which makes it easy to gain access and use this system in any future developments. Finally, we discuss the potential extensions that can be added to our framework as future contributions. The repository for this framework is freely available at https://github.com/SalarHoushvand/discrete-math-restfulAPI.

Additional Information

Publication

Thesis

Language: English

Date: 2020

Keywords

API, Auto Question Generation, Discrete Mathematics, Flask, Personalized Learning, Python

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