TITLE: 
Reimagining Textbooks Through the Data Lens

ABSTRACT:
Textbooks are the primary vehicles for delivering subject knowledge to the
students and are known to be the educational input most consistently
associated with improvements in student learning. With the emergence of
abundant online content, cloud computing, and electronic reading devices,
textbooks are poised for transformative changes. Inspired by the emergence of
the electronic medium for “printing” and “distributing” textbooks, we present
our early explorations into developing a data mining based approach for
enhancing the quality of electronic textbooks. Specifically, we first describe
a diagnostic tool for authors and educators to algorithmically identify
deficiencies in textbooks. We then discuss techniques for algorithmically
augmenting different sections of a book with links to selective content mined
from the Web.

Our tool for diagnosing deficiencies consists of two components. Abstracting
from the education literature, we identify the following properties of good
textbooks: (1) Focus: Each section explains few concepts, (2) Unity: For every
concept, there is a unique section that best explains the concept, and (3)
Sequentiality: Concepts are discussed in a sequential fashion so that a
concept is explained prior to occurrences of this concept or any related
concept. Further, the tie for precedence in presentation between two mutually
related concepts is broken in favor of the more significant of the two. The
first component provides an assessment of the extent to which these properties
are followed in a textbook and quantifies the comprehension load that a
textbook imposes on the reader due to non-sequential presentation of concepts.
The second component identifies sections that are not written well and can
benefit from further exposition. We propose a probabilistic decision model for
this purpose, which is based on the syntactic complexity of writing and the
notion of the dispersion of key concepts mentioned in the section.

For augmenting a section of a textbook, we first identify the set of key
concept phrases contained in a section. Using these phrases, we find web
articles that represent the central concepts presented in the section and
endow the section with links to them. We also describe techniques for finding
images that are most relevant to a section of the textbook, while respecting
the constraint that the same image is not repeated in different sections of
the same chapter. 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.

We finally provide the results of applying the proposed techniques to a corpus
of widely-used, high school textbooks published by the National Council of
Educational Research and Training, India. We consider books from grades
IX--XII, covering four broad subject areas, namely, Sciences, Social Sciences,
Commerce, and Mathematics. The preliminary results are encouraging and
indicate that developing technological approaches to embellishing textbooks
could be a promising direction for research.

* Based on joint work with Anitha Kannan, Krishnaram Kenthapadi, Sreenivas
  Gollapudi, and others.