Thursday, 11/21 @ 8:00pm
Reception to follow
Friday, 11/22 @ 2:35pm
Wege Auditorium (TCL 123).
Kathi Fisler ’91 survived her early attempts to learn computing through the patience and support of the Williams CS faculty. She headed to graduate school after being overwhelmed at a summer internship interview and never looked back (though she still misses the purple mountains). She is currently a Professor (Research) of Computer Science at Brown University and co-director of Bootstrap (a national K-12 outreach program for integrating computing into existing classes). Her current research focuses on computing education, with an emphasis on how people reason with and about formal systems. Outside of CS, she likes a good jigsaw puzzle, a bad pun, and a nice hike.
Thursday, November 21 @ 8:00pm
“Curriculum Design as an Engineering Problem: Lessons from the Field”
New computing curricula are being created every day. Seemingly every permutation of words like “teach”, “kids”, “code”, and “CS” has been turned into an organization (or company). Technologists everywhere are either being drafted to weigh in on curricula…or are doing so anyway.
Everybody’s got an opinion or three. But in the current and foreseeable reality of computer science education in the USA (and in many other countries), what does reality look like and how can we be effective in it? Might it even be that by working with reality, we may actually get better outcomes than if we ignored it?
This talk will distill lessons from Bootstrap, one of the largest computing outreach programs in the USA.
Joint work with Emmanuel Schanzer and Shriram Krishnamurthi.
Friday, November 22 @ 2:35pm
“In Defense of Little Code”
Big Code is all the rage. IDE builders and people who know static analysis see a wealth of opportunity to generate data to study how people program. What better way to identify coders’ skills and confusions than to harvest their IDE interactions, compilation attempts, error messages, and code evolution within and across assignments? Won’t this also revolutionize programming education? Unfortunately, this tool-builder’s dream often leaves instructors without essential information: why are students doing what they do? Unpacking this requires understanding students’ design choices in focused contexts, using Little Code and qualitative methods.
This talk will present several studies on how students learn program design through Big and Little Data about Big and Little Code. We’ll discuss how students choose program structure, how language choice interacts with program structure, and what misconceptions students have about semantics. We’ll also discuss possible implications of machine learning for K-12 and introductory college CS education. The talk aims to raise appreciation and questions about balancing the Big and the Little for improving programming education at many levels.