DocumentCode
245041
Title
Learning to Grade Student Programs in a Massive Open Online Course
Author
Drummond, Anna ; Yanxin Lu ; Chaudhuri, Swarat ; Jermaine, Christopher ; Warren, Joe ; Rixner, Scott
Author_Institution
Rice Univ., Houston, TX, USA
fYear
2014
fDate
14-17 Dec. 2014
Firstpage
785
Lastpage
790
Abstract
We study the problem of automatically evaluating the quality of computer programs produced by students in a very large, online, interactive programming course (or "MOOC"). Automatically evaluating interactive programs (such as computer games) is not easy because such programs lack any sort of well-defined logical specification. As an alternative, we devise some simple statistical approaches to assigning a score to a student-produced code.
Keywords
computer aided instruction; computer games; computer science education; educational administrative data processing; educational courses; formal specification; programming; statistical analysis; computer games; computer programs; interactive programming course; massive open online course; quality evaluation; statistical approaches; student program grading; student-produced code; well-defined logical specification; Computational modeling; Games; Libraries; Measurement; Peer-to-peer computing; Programming; Prototypes;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining (ICDM), 2014 IEEE International Conference on
Conference_Location
Shenzhen
ISSN
1550-4786
Print_ISBN
978-1-4799-4303-6
Type
conf
DOI
10.1109/ICDM.2014.142
Filename
7023401
Link To Document