%0 Conference Paper %1 citeulike:14367879 %A Hovemeyer, David %A Hellas, Arto %A Petersen, Andrew %A Spacco, Jaime %B Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education %C New York, NY, USA %D 2017 %I ACM %K log-mining program-analysis %P 709 %R 10.1145/3017680.3022418 %T Progsnap: Sharing Programming Snapshots for Research (Abstract Only) %U http://dx.doi.org/10.1145/3017680.3022418 %X Recent years have seen increasing interest in using programming snapshot data for education research. One barrier to such research, especially for studies involving data from multiple institutions, is that the data is in a wide variety of native formats, and those formats may not be conducive to automated analysis. To overcome this barrier, we propose a structured data model and archival data format called Progsnap (https://cloudcoderdotorg.github.io/progsnap-spec/). Progsnap is designed to be a neutral export format, is currently supported by two open-source programming exercise systems, and we believe will be an easy target for data export from other systems. An open source Python library makes it easy to automate analysis of Progsnap datasets. %@ 978-1-4503-4698-6

@inproceedings{citeulike:14367879, abstract = {{Recent years have seen increasing interest in using programming snapshot data for education research. One barrier to such research, especially for studies involving data from multiple institutions, is that the data is in a wide variety of native formats, and those formats may not be conducive to automated analysis. To overcome this barrier, we propose a structured data model and archival data format called Progsnap (https://cloudcoderdotorg.github.io/progsnap-spec/). Progsnap is designed to be a neutral export format, is currently supported by two open-source programming exercise systems, and we believe will be an easy target for data export from other systems. An open source Python library makes it easy to automate analysis of Progsnap datasets.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {New York, NY, USA}, author = {Hovemeyer, David and Hellas, Arto and Petersen, Andrew and Spacco, Jaime}, biburl = {https://www.bibsonomy.org/bibtex/2a9afbdc32fc69d669888f5fc47af8dd3/aho}, booktitle = {Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education}, citeulike-article-id = {14367879}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=3022418}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/3017680.3022418}, doi = {10.1145/3017680.3022418}, interhash = {2ac5358e190b662b670b834a7f02f3f5}, intrahash = {a9afbdc32fc69d669888f5fc47af8dd3}, isbn = {978-1-4503-4698-6}, keywords = {log-mining program-analysis}, location = {Seattle, Washington, USA}, pages = 709, posted-at = {2017-06-04 21:49:00}, priority = {2}, publisher = {ACM}, series = {SIGCSE '17}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Progsnap: Sharing Programming Snapshots for Research (Abstract Only)}}, url = {http://dx.doi.org/10.1145/3017680.3022418}, year = 2017 }