Enriching textbooks through data mining
Proceedings of the First ACM Symposium on Computing for Development
Identifying enrichment candidates in textbooks
Proceedings of the 20th international conference companion on World wide web
Hi-index | 0.00 |
Education is acknowledged to be the primary vehicle for improving the economic well-being of people [1,6]. Textbooks have a direct bearing on the quality of education imparted to the students as they are the primary conduits for delivering content knowledge [9]. They are also indispensable for fostering teacher learning and constitute a key component of the ongoing professional development of the teachers [5,8]. Many textbooks, particularly from emerging countries, lack clear and adequate coverage of important concepts [7]. In this talk, we present our early explorations into developing a data mining based approach for enhancing the quality of textbooks. We discuss techniques for algorithmically augmenting different sections of a book with links to selective content mined from the Web. For finding authoritative articles, we first identify the set of key concept phrases contained in a section. Using these phrases, we find web (Wikipedia) articles that represent the central concepts presented in the section and augment the section with links to them [4]. We also describe a framework for finding images that are most relevant to a section of the textbook, while respectingglobal relevancy to the entire chapter to which the section belongs. We pose this problem of matching images to sections in a textbook chapter as an optimization problem and present an efficient algorithm for solving it [2].