%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 = {2018-03-19T12:24:51.000+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/aho}, 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 = {2018-03-19T12:24:51.000+0100}, title = {{Data-Driven Feedback Generator for Online Programing Courses}}, url = {http://dx.doi.org/10.1145/3051457.3053999}, year = 2017 }