When computer science first emerged as a discipline around 75 years ago, calculus played almost no role in the typical computer science curriculum. However, the rise of modern artificial intelligence and robotics has made it essential for computer scientists to understand and apply calculus and probability when designing algorithms for these domains.
“Calculus has come back into computer science with a vengeance,” GW Computer Science Professor Rahul Simha shared.
For decades, computer science undergraduates studied mathematical topics under the umbrella of discrete structures. In response to the emergence of AI and robotics, and the resulting need for continuous structures in calculus and probability, Prof. Simha began developing course material on continuous structures in 2008.
Students responded positively because the course integrated computer science applications and used programming as a learning tool.
Simha was recently awarded a three-year National Science Foundation (NSF) grant to continue his work equipping computer science students with the calculus and probability tools they need to excel in AI, robotics, and other emergent areas of their field.
The newly awarded grant will support his ongoing work and facilitate collaboration with colleagues at the Rochester Institute of Technology and Regis University, where faculty aim to adopt Simha’s course materials into their curriculum.
“Regis is excited to collaborate with GWU and build on its leadership in developing this innovative NSF-supported approach to teaching continuous structures in meaningful computing contexts,” said Regis University Professor Shari Plantz-Masters.
“By bringing the approach to our predominantly nontraditional, online student population, we have an opportunity to broaden the project’s impact while addressing the growing need for computer science students to develop fluency in continuous mathematics alongside the field’s traditional emphasis on discrete mathematics,” Plantz-Masters shared.
The team will also examine conceptual challenges faced by students learning through this approach and examine the effectiveness of computational pedagogy.
“The story of calculus and calculus education is over 300 years old,” Simha explained.
“You could say it’s mostly driven by the needs of physics and engineering. Yet, the calculus needed for computer science is somewhat different, both in subtopics and applications.”
“And just like symbolic math is one way to learn mathematical concepts, using programming is another - and is especially suited to computer science students. Both approaches, symbolic and computational, complement each other nicely,” he said.
“The combination of computer science applications and the programming pedagogy is what makes the course a good match for CS students.”