Manually providing feedback for programming assignments is a tedious task in traditional classroom education. The challenge increases drastically in Massive open online courses (MOOCs), where the student-teacher ratio can reach thousands to one or even millions to one. Despite the necessity, the current automated feedback approaches suffer from significant weaknesses: inability to scale to larger programs, manual involvement of teacher effort, and lack of precision for pin-pointing errors. We present a technique to tackle these challenges by developing a data-driven automated grader, iGrader, capable of generating instant and precise feedback for programming assignments.
%0 Conference Paper
%1 citeulike:14346953
%A Wang, Ke
%A Lin, Benjamin
%A Rettig, Bjorn
%A Pardi, Paul
%A Singh, Rishabh
%B Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale
%C New York, NY, USA
%D 2017
%I ACM
%K assessment feedback las2017 mooc programming progtutor
%P 257--260
%R 10.1145/3051457.3053999
%T Data-Driven Feedback Generator for Online Programing Courses
%U http://dx.doi.org/10.1145/3051457.3053999
%X Manually providing feedback for programming assignments is a tedious task in traditional classroom education. The challenge increases drastically in Massive open online courses (MOOCs), where the student-teacher ratio can reach thousands to one or even millions to one. Despite the necessity, the current automated feedback approaches suffer from significant weaknesses: inability to scale to larger programs, manual involvement of teacher effort, and lack of precision for pin-pointing errors. We present a technique to tackle these challenges by developing a data-driven automated grader, iGrader, capable of generating instant and precise feedback for programming assignments.
%@ 978-1-4503-4450-0
@inproceedings{citeulike:14346953,
abstract = {{Manually providing feedback for programming assignments is a tedious task in traditional classroom education. The challenge increases drastically in Massive open online courses (MOOCs), where the student-teacher ratio can reach thousands to one or even millions to one. Despite the necessity, the current automated feedback approaches suffer from significant weaknesses: inability to scale to larger programs, manual involvement of teacher effort, and lack of precision for pin-pointing errors. We present a technique to tackle these challenges by developing a data-driven automated grader, iGrader, capable of generating instant and precise feedback for programming assignments.}},
added-at = {2017-11-15T17:02:25.000+0100},
address = {New York, NY, USA},
author = {Wang, Ke and Lin, Benjamin and Rettig, Bjorn and Pardi, Paul and Singh, Rishabh},
biburl = {https://www.bibsonomy.org/bibtex/22ef7681ccc3412d46f69a153c3afa99c/brusilovsky},
booktitle = {Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale},
citeulike-article-id = {14346953},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=3053999},
citeulike-linkout-1 = {http://dx.doi.org/10.1145/3051457.3053999},
comment = {(private-note)Yet another data-driven feedback generation!},
doi = {10.1145/3051457.3053999},
interhash = {ecb603b5fbca34ea0e273d792af0b0a0},
intrahash = {2ef7681ccc3412d46f69a153c3afa99c},
isbn = {978-1-4503-4450-0},
keywords = {assessment feedback las2017 mooc programming progtutor},
location = {Cambridge, Massachusetts, USA},
pages = {257--260},
posted-at = {2017-04-29 19:45:10},
priority = {2},
publisher = {ACM},
series = {L@S '17},
timestamp = {2018-08-13T14:00:24.000+0200},
title = {Data-Driven Feedback Generator for Online Programing Courses},
url = {http://dx.doi.org/10.1145/3051457.3053999},
year = 2017
}