%0 Conference Paper %1 Rivers_2016 %A Rivers, Kelly %A Harpstead, Erik %A Koedinger, Ken %B Proceedings of the 2016 ACM Conference on International Computing Education Research - ICER \textquotesingle16 %D 2016 %I ACM Press %K learning-curve programming student-modeling %R 10.1145/2960310.2960333 %T Learning Curve Analysis for Programming %U https://doi.org/10.1145%2F2960310.2960333 %X The recent surge in interest in using educational data mining on student written programs has led to discoveries about which compiler errors students encounter while they are learning how to program. However, less attention has been paid to the actual code that students produce. In this paper, we investigate programming data by using learning curve analysis to determine which programming elements students struggle with the most when learning in Python. Our analysis extends the traditional use of learning curve analysis to include less structured data, and also reveals new possibilities for when to teach students new programming concepts. One particular discovery is that while we find evidence of student learning in some cases (for example, in function definitions and comparisons), there are other programming elements which do not demonstrate typical learning. In those cases, we discuss how further changes to the model could affect both demonstrated learning and our understanding of the different concepts that students learn.

@inproceedings{Rivers_2016, abstract = {The recent surge in interest in using educational data mining on student written programs has led to discoveries about which compiler errors students encounter while they are learning how to program. However, less attention has been paid to the actual code that students produce. In this paper, we investigate programming data by using learning curve analysis to determine which programming elements students struggle with the most when learning in Python. Our analysis extends the traditional use of learning curve analysis to include less structured data, and also reveals new possibilities for when to teach students new programming concepts. One particular discovery is that while we find evidence of student learning in some cases (for example, in function definitions and comparisons), there are other programming elements which do not demonstrate typical learning. In those cases, we discuss how further changes to the model could affect both demonstrated learning and our understanding of the different concepts that students learn. }, added-at = {2020-04-16T15:15:55.000+0200}, author = {Rivers, Kelly and Harpstead, Erik and Koedinger, Ken}, biburl = {https://www.bibsonomy.org/bibtex/2e977df5630a8d3f53fcab7e9684d1e53/brusilovsky}, booktitle = {Proceedings of the 2016 {ACM} Conference on International Computing Education Research - {ICER} {\textquotesingle}16}, description = {Learning Curve Analysis for Programming | Proceedings of the 2016 ACM Conference on International Computing Education Research}, doi = {10.1145/2960310.2960333}, interhash = {b698a64ffc3da12ec5e81cca6546e1ca}, intrahash = {e977df5630a8d3f53fcab7e9684d1e53}, keywords = {learning-curve programming student-modeling}, publisher = {{ACM} Press}, timestamp = {2020-04-16T15:16:30.000+0200}, title = {Learning Curve Analysis for Programming}, url = {https://doi.org/10.1145%2F2960310.2960333}, year = 2016 }