Nathan Carter (Bentley University): Lurch: software for immediate feedback for students in a first proof course (School Seminar)

Event details

  • When: 25th September 2018 14:00 - 15:00
  • Where: Cole 1.33a
  • Series: School Seminar Series
  • Format: Seminar


Lurch is an open-source word processor that can check the steps in students’ mathematical proofs. Users write in a natural language, but mark portions of a document as meaningful, so the software can distinguish content for human readers from content it should analyze.

This talk begins with an overview of the most recent release of the system, the ways in which it impacts students’ learning of mathematical proofs, and how it needs to be improved in the future. I will then cover how we are making those improvements in the next version, which will lead naturally to an introduction of the Lurch Web Platform, a foundational set of tools that we will use to bring the project to the web.

That platform is available on GitHub for other mathematical software developers to use in their own projects. It includes a web editor with mathematical typesetting, an interface for marking up documents with mathematical (or other structured) meaning, OpenMath support, meaning visualization tools, and document dependence and sharing features, among others.

Speaker Bio:

Nathan Carter uses computer science to advance mathematics. He writes open source mathematics software for university mathematics education, in areas including mathematical logic and abstract algebra visualization. He is a past winner of the Mathematical Association of America’s Henry L. Alder Award for Distinguished Teaching by a Beginning College or University Mathematics Faculty Member and his first book, Visual Group Theory, won the 2012 Beckenbach Book Prize from that same society. His second book, Introduction to the Mathematics of Computer Graphics, was published in 2016. His current book project will be an edited volume entitled Data Science for Mathematicians, intended to help mathematics faculty make the transition into teaching and doing research in the fast-growing field of data science.