Supporting novices in learning programming on-the-fly using examples
Many people, including children, begin learning programming independently in open-ended contexts. This prevents them from receiving feedback that would introduce them to new skills. In this talk, I will present a system called the Example Guru, which suggests new skills to novice programmers using example code. Both in lab studies and the wild, novices chose to access suggestions significantly more often than common forms of support, like documentation or tutorials. Accessing suggestions often led to more use of new code. I will also discuss my approach for semi-automatically generating suggestions and examples. This approach generated both a set of suggestions similar to an expert hand-authored set, as well as an additional set of original suggestions. This type of support for independent novice programmers has the potential to significantly help the large population of non-expert programmers learning on-the-fly as they work toward their own goals.
Michelle is a PhD candidate in Computer Science at Washington University in St. Louis, advised by Caitlin Kelleher. Her research is in human-computer interaction, focusing on improving scalable support for independent learning. She developed her work in the context of Looking Glass (http://lookingglass.wustl.edu/), a freely available programming tool for creating 3D animated stories. Through her research, she has both increased understanding of non-expert programmers as well as designed and built systems to support them. Michelle received the Spencer T. and Ann W. Olin Fellowship for Women in Graduate Study, an NSF Graduate Fellowship Honorable Mention, and the 2017 best paper award at the IEEE Symposium on Visual Languages and Human-Centric Computing.