Marios Kokkodis, Anitha Kannan, and Krishnaram Kenthapadi
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. Towards this goal, techniques have been proposed to automatically augment textbook sections with relevant web content such as online educational videos. However, a highly relevant video can be created at a granularity that may not mimic the organization of the textbook. We focus on the video assignment problem: Given a candidate set of relevant educational videos for augmenting an electronic textbook, how do we assign the videos at appropriate locations in the textbook? 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.
|Published in||International Conference on Educational Data Mining (EDM)|
|Publisher||International Educational Data Mining Society|
Marios Kokkodis, Anitha Kannan, and Krishnaram Kenthapadi. Assigning Videos to Textbooks at Appropriate Granularity, ACM, March 2014.