We present our early explorations into developing a data mining based approach for enhancing the quality of textbooks. We describe a diagnostic tool to algorithmically identify deficient sections in textbooks. We also discuss techniques for algorithmically augmenting textbook sections with links to selective content mined from the Web. Our evaluation, employing widely-used textbooks from India, indicates that developing technological approaches to help improve textbooks holds promise.
%0 Journal Article
%1 citeulike:14087143
%A Agrawal, Rakesh
%A Gollapudi, Sreenivas
%A Kannan, Anitha
%A Kenthapadi, Krishnaram
%C New York, NY, USA
%D 2012
%I ACM
%J SIGKDD Explor. Newsl.
%K datamining electronic-textbook
%N 2
%P 7--19
%R 10.1145/2207243.2207246
%T Data Mining for Improving Textbooks
%U http://dx.doi.org/10.1145/2207243.2207246
%V 13
%X We present our early explorations into developing a data mining based approach for enhancing the quality of textbooks. We describe a diagnostic tool to algorithmically identify deficient sections in textbooks. We also discuss techniques for algorithmically augmenting textbook sections with links to selective content mined from the Web. Our evaluation, employing widely-used textbooks from India, indicates that developing technological approaches to help improve textbooks holds promise.
@article{citeulike:14087143,
abstract = {{We present our early explorations into developing a data mining based approach for enhancing the quality of textbooks. We describe a diagnostic tool to algorithmically identify deficient sections in textbooks. We also discuss techniques for algorithmically augmenting textbook sections with links to selective content mined from the Web. Our evaluation, employing widely-used textbooks from India, indicates that developing technological approaches to help improve textbooks holds promise.}},
added-at = {2018-03-19T12:24:51.000+0100},
address = {New York, NY, USA},
author = {Agrawal, Rakesh and Gollapudi, Sreenivas and Kannan, Anitha and Kenthapadi, Krishnaram},
biburl = {https://www.bibsonomy.org/bibtex/2c68c29c3b17f0d11f3c23392c67c0515/aho},
citeulike-article-id = {14087143},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=2207246},
citeulike-linkout-1 = {http://dx.doi.org/10.1145/2207243.2207246},
doi = {10.1145/2207243.2207246},
interhash = {bec810b8063c81232d3ede392b332df6},
intrahash = {c68c29c3b17f0d11f3c23392c67c0515},
issn = {1931-0145},
journal = {SIGKDD Explor. Newsl.},
keywords = {datamining electronic-textbook},
month = may,
number = 2,
pages = {7--19},
posted-at = {2016-06-30 14:32:11},
priority = {2},
publisher = {ACM},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{Data Mining for Improving Textbooks}},
url = {http://dx.doi.org/10.1145/2207243.2207246},
volume = 13,
year = 2012
}