Automatic SQL query generation
| dc.access.option | Restricted Campus Access Only | |
| dc.contributor.advisor | Gudivada, Venkat N | |
| dc.contributor.author | Arbabifard, Kamyar | |
| dc.contributor.department | Computer Science | |
| dc.date.accessioned | 2017-08-09T16:48:13Z | |
| dc.date.available | 2018-03-14T18:00:43Z | |
| dc.date.created | 2017-08 | |
| dc.date.issued | 2017-07-19 | |
| dc.date.submitted | August 2017 | |
| dc.date.updated | 2017-08-07T22:24:16Z | |
| dc.degree.department | Computer Science | |
| dc.degree.discipline | MS-Computer Science | |
| dc.degree.grantor | East Carolina University | |
| dc.degree.level | Masters | |
| dc.degree.name | M.S. | |
| dc.description.abstract | 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. | |
| dc.embargo.lift | 2018-02-01 | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10342/6401 | |
| dc.language.iso | en | |
| dc.publisher | East Carolina University | |
| dc.subject | Personalized Learning | |
| dc.subject | Question Generation | |
| dc.subject.lcsh | SQL (Computer program language) | |
| dc.subject.lcsh | MOOCs (Web-based instruction) | |
| dc.subject.lcsh | Educational evaluation | |
| dc.title | Automatic SQL query generation | |
| dc.type | Master's Thesis | |
| dc.type.material | text |