Development of Debugging Exercise Extraction System using Learning History We have proposed an editing history visualization system which can confirm where and how the learner modified program. We utilized this system for actual flipped classroom and stored a large amount of learning logs. This learning log contains all the source code in the process of being modified until the program is completed. We developed a debugging exercise extraction system to automatically generate problems for debugging practice from this learning log. The debugging exercise extraction tool we developed extracted 18,680 source codes (which became practice problems) that included syntactic errors that could be used as a debugging exercise from 16 weeks of program edit history data (total number is 31,562 files). The execution time was 488 seconds. Since it can be analyzed only once every six months, we believe it is a sufficiently practical execution time.

Code Localization in Programming Screencasts Programming screencasts are growing in popularity and are often used by developers as a learning source. The source code shown in these screencasts is often not available for download or copypasting. Without having the code readily available, developers have to frequently pause a video to transcribe the code. This is timeconsuming and reduces the effectiveness of learning from videos. Recent approaches have applied Optical Character Recognition (OCR) techniques to automatically extract source code from programming screencasts. One of their major limitations, however, is the extraction of noise such as the text information in the menu, package hierarchy, etc. due to the imprecise approximation of the code location on the screen. This leads to incorrect, unusable code. We aim to address this limitation and propose an approach to significantly improve the accuracy of code localization in programming screencasts, leading to a more precise code extraction. Our approach uses a Convolutional Neural Network to automatically predict the exact location of code in an image. We evaluated our approach on a set of frames extracted from 450 screencasts covering Java, C, and Python programming topics. The results show that our approach is able to detect the area containing the code with 94% accuracy and that our approach significantly outperforms previous work. We also show that applying OCR on the code area identified by our approach leads to a 97% match with the ground truth on average, compared to only 31% when OCR is applied to the entire frame.