This is intended to be an informal talk describing some teaching and learning experiments I've tried on a freshman math class for ELITE (academically very strong) engineering undergraduate students with very encouraging results. Besides the "usual" philosophy of flipped classrooms (students learn basic content at home via videos/automated exercises, and do learning exercises requiring higher level of creativity in class via groupwork), some of the other strategies attempted include: * Setting up a peer-knowledge exchange marketplace (students are incentivized to teach each other, incentivized by bonus points), * Gamification of problem-setting/solving (students are incentivized to set "interesting" questions for each other) * Advanced group projects (this was a freshman linear algebra/vector calculus course, but by semester-end students students had impressive projects (final year thesis level) on applications of linear algebra such as quantum search algorithms, face recognition, Privacy Preserving codes, etc) * Computer Aided Math instruction (all classes (and exams!) were in a computer lab, and students were encouraged to use symbolic math software (Maxima) to outsource the "mechanical" aspects of calculations.) The topics to be discussed will be audience-driven. Besides nuts-and-bolts strategies behind the class that helped all this work (careful workflow management, design of incentive systems, regular two-way feedback sessions, understanding students' love-hate relationship with the class, etc), I can also talk about my hunches about the underpinning social interactions that made this a wildly successful class, for which both the students and instructors worked harder than they have ever before, and potential philosophical implications for the classroom of the future (as instructors, are we automating ourself out of the knowledge delivery business by developing electronic content? Based on my experience I'd say definitely not). My host, Prof. Bikash Dey, has also indicated that several interesting flipped/elearning courses have been offered at IITB, and I'd love to hear about them; have a discussion.
Sidharth Jaggi received his Bachelor of Technology degree from the Indian Institute of Technology in 2000, and his Master of Science and Ph.D. degrees from the California institute of Technology in 2001 and 2006 respectively, all in electrical engineering. He was awarded the Caltech Division of Engineering Fellowship 2001-'02, and the Microsoft Research Fellowship for the years 2002-'04. He interned at Microsoft Research, (Redmond, WA, USA) in the summers of 2002-'03 and engaged in research on network coding. He spent 2006 as a Postdoctoral Associate at the Laboratory of Information and Decision Systems at the Massachusetts Institute of Technology. He joined the Department of Information Engineering, the Chinese University of Hong Kong in 2007, where he is currently an Associate Professor. Sidharth's research interests lie at the intersection of information theory, algorithms, and networking.