Textbooks for developing regions

Abstract

Listen

Translate article

Education is known to be the key determinant of economic growth and prosperity [9, 13]. While the issues in devising a high-quality educational system are multi-faceted and complex, textbooks are acknowledged to be the educational input most consistently associated with gains in student learning [12]. Particularly in developing regions, they are the primary conduits for delivering content knowledge to the students and the teachers base their lesson plans primarily on the material given in textbooks[8]. However, many textbooks from these regions suffer from the lack of clarity of language as well as the inadequacy of information provided [10]. Because of cost considerations, textbooks are often compressed into fewer pages resulting in poor exposition of subject matter [11].Considerable research has gone into investigating what makes for good textbooks [7, 11]. While there are multiple dimensions that determine the quality of a textbook, there is general agreement that good textbooks should provide adequate coverage of important concepts and be organized in a systematically progressive fashion so that students acquire new knowledge and learn new concepts based on known items of information. We present our early explorations into algorithmic approaches for enhancing the quality of textbooks. Specifically, we first describe a diagnostic tool for authors and educators to identify deficiencies in textbooks. We then discuss techniques for 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 [2, 3]. 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 [5].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 [6]. 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 [4].We also provide 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 (NCERT), India. We consider books from grades IX-XII, covering four broad subject areas, namely, Sciences, Social Sciences, Commerce, and Mathematics. The results are encouraging and indicate that technological approaches can help address deficiencies in textbooks.