%0 Conference Paper %1 citeulike:13664994 %A Spacco, Jaime %A Denny, Paul %A Richards, Brad %A Babcock, David %A Hovemeyer, David %A Moscola, James %A Duvall, Robert %B Proceedings of the 46th ACM Technical Symposium on Computer Science Education %C New York, NY, USA %D 2015 %I ACM %K log-mining program-analysis programming progtutor %P 18--23 %R 10.1145/2676723.2677297 %T Analyzing Student Work Patterns Using Programming Exercise Data %U http://dx.doi.org/10.1145/2676723.2677297 %X Web-based programming exercises are a useful way for students to practice and master essential concepts and techniques presented in introductory programming courses. Although these systems are used fairly widely, we have a limited understanding of how students use these systems, and what can be learned from the data collected by these systems. In this paper, we perform a preliminary exploratory analysis of data collected by the CloudCoder programming exercise system from five introductory courses taught in two programming languages across three colleges and universities. We explore a number of interesting correlations in the data that confirm existing hypotheses. Finally, and perhaps most importantly, we demonstrate the effectiveness and future potential of systems like CloudCoder to help us study novice programmers. %@ 978-1-4503-2966-8

@inproceedings{citeulike:13664994, abstract = {{Web-based programming exercises are a useful way for students to practice and master essential concepts and techniques presented in introductory programming courses. Although these systems are used fairly widely, we have a limited understanding of how students use these systems, and what can be learned from the data collected by these systems. In this paper, we perform a preliminary exploratory analysis of data collected by the CloudCoder programming exercise system from five introductory courses taught in two programming languages across three colleges and universities. We explore a number of interesting correlations in the data that confirm existing hypotheses. Finally, and perhaps most importantly, we demonstrate the effectiveness and future potential of systems like CloudCoder to help us study novice programmers.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {New York, NY, USA}, author = {Spacco, Jaime and Denny, Paul and Richards, Brad and Babcock, David and Hovemeyer, David and Moscola, James and Duvall, Robert}, biburl = {https://www.bibsonomy.org/bibtex/2c24ecb5ea323984c03f18f16d54dff7c/aho}, booktitle = {Proceedings of the 46th ACM Technical Symposium on Computer Science Education}, citeulike-article-id = {13664994}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=2677297}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/2676723.2677297}, doi = {10.1145/2676723.2677297}, interhash = {81dc9b1f0ef22b4293c7cd1f83c3ab7c}, intrahash = {c24ecb5ea323984c03f18f16d54dff7c}, isbn = {978-1-4503-2966-8}, keywords = {log-mining program-analysis programming progtutor}, location = {Kansas City, Missouri, USA}, pages = {18--23}, posted-at = {2017-06-04 21:51:46}, priority = {2}, publisher = {ACM}, series = {SIGCSE '15}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Analyzing Student Work Patterns Using Programming Exercise Data}}, url = {http://dx.doi.org/10.1145/2676723.2677297}, year = 2015 }