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001 on1001281754;

003 OCoLC;

005 20171031024952.0;

006 m o d ;

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

008 170816s2017 ncua obm 000 0 eng d;

035 a| (Sirsi) o1001281754;

035 a| (OCoLC)1001281754;

049 a| EREE;

090 a| QA76.73.S67;

100 1 a| Arbabifard, Kamyar,e| author.?| UNAUTHORIZED;

245 10 a| Automatic SQL query generation /c| by Kamyar Arbabifard.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2017.;

300 a| 68 pages :b| illustrations (some color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 4.574 MB2| rda;

538 a| System requirements: Adobe Reader.;

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

502 b| M.S.c| East Carolina Universityd| 2017.;

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

500 a| Advisor: Venkat N. Gudivada;

500 a| Title from PDF t.p. (viewed October 23, 2017).;

520 3 a| Automatic generation of questions for learning assessment has been an area of research interest for long. The advent of Massive Open Online Courses (MOOCs) as well as the goal of providing immediate contextualized feedback to enhance student learning has created renewed interest in automatic question generation. This thesis motivates the automatic question generation problem, gives an overview of the current approaches, and describes the proposed novel approach to automatic generation of SQL queries using the notion of grammar graph.;

504 a| Includes bibliographical references.;

650 0 a| SQL (Computer program language)=| ^A270202;

650 0 a| MOOCs (Web-based instruction)=| ^A1260780;

650 0 a| Educational evaluation.=| ^A195382;

653 a| Personalized Learning;

653 a| Question Generation;

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/10342/6401;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 4733350;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4733350-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 8/16/2017x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4733350-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 8/16/2017x| ETDz| HERESOURCE;

Automatic SQL query generation / by Kamyar Arbabifard.

Author/creator	Arbabifard, Kamyar 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], 2017.
Description	68 pages : illustrations (some color)
Supplemental Content	Access via ScholarShip
Subjects	SQL (Computer program language) MOOCs (Web-based instruction) Educational evaluation.

Summary	Automatic generation of questions for learning assessment has been an area of research interest for long. The advent of Massive Open Online Courses (MOOCs) as well as the goal of providing immediate contextualized feedback to enhance student learning has created renewed interest in automatic question generation. This thesis motivates the automatic question generation problem, gives an overview of the current approaches, and describes the proposed novel approach to automatic generation of SQL queries using the notion of grammar graph.
General note	Presented to the faculty of the Department of Computer Science
General note	Advisor: Venkat N. Gudivada
General note	Title from PDF t.p. (viewed October 23, 2017).
Dissertation note	M.S. East Carolina University 2017.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Automatic SQL query generation / by Kamyar Arbabifard.

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