%0 Conference Paper %1 citeulike:13417204 %A Kokkodis, Marios %A Kannan, Anitha %A Kenthapadi, Krishnaram %B Proceedings of the First ACM Conference on Learning @ Scale Conference %C New York, NY, USA %D 2014 %I ACM %K electronic-textbook %P 199--200 %R 10.1145/2556325.2567880 %T Assigning Videos to Textbooks at Appropriate Granularity %U http://dx.doi.org/10.1145/2556325.2567880 %X The emergence of tablet devices, cloud computing, and abundant online multimedia content presents new opportunities to transform traditional paper-based textbooks into tablet-based electronic textbooks, and to further augment the educational experience by enriching them with relevant supplementary materials. Given a candidate set of relevant educational videos for augmenting an electronic textbook, how do we assign the videos at the appropriate granularity (a collection of logical units in the book)? We propose a rigorous formulation of the video assignment problem and present an algorithm for assigning each video to the optimum subset of logical units. Our experimental evaluation using a diverse collection of educational videos relevant to multiple chapters in a textbook demonstrates the efficacy of the proposed techniques for inferring the granularity at which a relevant video should be assigned. %@ 978-1-4503-2669-8

@inproceedings{citeulike:13417204, abstract = {{The emergence of tablet devices, cloud computing, and abundant online multimedia content presents new opportunities to transform traditional paper-based textbooks into tablet-based electronic textbooks, and to further augment the educational experience by enriching them with relevant supplementary materials. Given a candidate set of relevant educational videos for augmenting an electronic textbook, how do we assign the videos at the appropriate granularity (a collection of logical units in the book)? We propose a rigorous formulation of the video assignment problem and present an algorithm for assigning each video to the optimum subset of logical units. Our experimental evaluation using a diverse collection of educational videos relevant to multiple chapters in a textbook demonstrates the efficacy of the proposed techniques for inferring the granularity at which a relevant video should be assigned.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {New York, NY, USA}, author = {Kokkodis, Marios and Kannan, Anitha and Kenthapadi, Krishnaram}, biburl = {https://www.bibsonomy.org/bibtex/2a46a1f301ad773c3a2bf3974d977f6ba/aho}, booktitle = {Proceedings of the First ACM Conference on Learning @ Scale Conference}, citeulike-article-id = {13417204}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=2567880}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/2556325.2567880}, comment = {How to assign videos found on internet to chapters of textbooks Uses textbook models.}, doi = {10.1145/2556325.2567880}, interhash = {1e7ad7e6d36134dbbc1740dae831d78a}, intrahash = {a46a1f301ad773c3a2bf3974d977f6ba}, isbn = {978-1-4503-2669-8}, keywords = {electronic-textbook}, location = {Atlanta, Georgia, USA}, pages = {199--200}, posted-at = {2014-11-03 17:14:43}, priority = {2}, publisher = {ACM}, series = {L@S '14}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Assigning Videos to Textbooks at Appropriate Granularity}}, url = {http://dx.doi.org/10.1145/2556325.2567880}, year = 2014 }