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001 on1231501228;

003 OCoLC;

005 20210802095114.0;

006 m o d ;

007 cr unu||||||||;

008 210114s2020 ncua obm 000 0 eng d;

035 a| (Sirsi) o1231501228;

035 a| (OCoLC)1231501228;

049 a| EREE;

090 a| QA297.4;

100 1 a| Houshvand, Salar,e| author.?| UNAUTHORIZED;

245 12 a| A framework for automatically generating questions for topics in discrete mathematics /c| by Salar Houshvand.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2020.;

300 a| 65 pages :b| color illustrations;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 764.9 KB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2020.;

500 a| Presented to the faculty of the Department of Computer Science;

500 a| Advisor: Venkat Gudivada;

500 a| Title from PDF t.p. (viewed July 27, 2021).;

520 3 a| 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.;

504 a| Includes bibliographical references.;

650 0 a| Discrete mathematics.=| ^A1407758;

650 0 a| Individualized instruction.=| ^A1341;

650 0 a| Python (Computer program language)=| ^A809520;

653 a| API;

653 a| Auto Question Generation;

653 a| Flask;

700 1 a| Gudivada, Venkat N.e| degree supervisor.=| ^A1308613;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/8814;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5547870;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5547870-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 1/14/2021x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5547870-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 1/14/2021x| ETDz| HERESOURCE;

A framework for automatically generating questions for topics in discrete mathematics / by Salar Houshvand.

Author/creator	Houshvand, Salar author.
Other author	Gudivada, Venkat N. degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2020.
Description	65 pages : color illustrations
Supplemental Content	Access via ScholarShip
Subjects	Discrete mathematics. Individualized instruction. Python (Computer program language)

Summary	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.
General note	Presented to the faculty of the Department of Computer Science
General note	Advisor: Venkat Gudivada
General note	Title from PDF t.p. (viewed July 27, 2021).
Dissertation note	M.S. East Carolina University 2020.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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A framework for automatically generating questions for topics in discrete mathematics / by Salar Houshvand.

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