A Pedagogical Library for analyzing student’s Python work and delivering feedback. Uses various tools including a type inferencer, flow analyzer, and code pattern matching.
Many Introductory Computer Science courses rely on abstract problems, which clashes with students’ experiences in a media and data-rich world. By using real data sources, academic problems are contextualized, thereby
However, there are drawbacks to using these data sources:
To overcome these challenges, we’ve created:
Developed under Dr. Eli Tilevich,Dr. Cliff Shaffer, and Dr. Dennis Kafura as part of two NSF Grants.
BlockPy is a web-based Python environment that lets you work with blocks, text, or both. Designed for Data Science and equipped with powerful tools like the State Explorer and Guided Feedback, the goal of BlockPy is to let you solve authentic, real-world problems.
I’ve helped to teach and develop a new course on “Computational Thinking” at Virginia Tech, emphasizing Abstractions and Algorithms through the use of Data Science. This takes advantage of both BlockPy and CORGIS. We’ve even been written up in a local newspaper!
An instructionally designed curriculum for teaching intro python, open-sourced and accessible for instructors to adopt.