Abstract

In the context of education, the proliferation of low-cost and increasingly accessible cloud-connected tablet devices present important new opportunities. With printed textbooks being digitized, as is, our research address the following question: How do we enrich the experience of reading from digitized printed books, on cloud-connected devices? Taking into account the vast amount of existing textbooks designed for traditional printed medium and the potential for enabling new kinds of functionalities through the medium of electronic textbooks, we present the results of our research into algorithmically diagnosing and enhancing the quality of textbooks. Specifically, we first describe a diagnostic tool for authors and educators to identify weaknesses in a textbook. We then discuss techniques for augmenting different sections of a textbook with links to selective web content (web articles, images & videos) and providing easy access to concepts explained elsewhere in the book that are necessary for understanding the present section. We have used a corpus of high-school and graduate level textbooks from USA and India to drive our research. We have also built a demonstration of the ideas running on Microsoft Surface and other Windows 8 devices as well as on Aakash, the low cost tablet being developed by the Indian government for distribution to millions of students.

Bio: Krishnaram Kenthapadi is a Researcher at Microsoft Research Silicon Valley in Mountain View, California. He graduated with a Ph.D. degree in Computer Science from Stanford University in 2006, under the supervision of Professor Rajeev Motwani. His Ph.D. thesis is titled “Models and Algorithms for Data Privacy”. Before joining Stanford, he received his Bachelors degree in Computer Science and Engineering from Indian Institute of Technology-Madras. His recent research interest is in applying algorithmic and data mining techniques for improving education. His other interests include algorithms for the web and social networks, privacy preserving data mining, hashing and load balancing. He received the Best Student Paper award in the SODA 2006 conference. His WWW 2009 paper was nominated for the Best Paper Award.