Strategies for using data analytics in testing the readability levels of textbooks: it's time to get serious

The idea that education in America is deteriorating is emotionally charged and controversial. While there is no disputing that education levels in the United States continue to rise, there is also a pervasive notion that this was accomplished by gradually reducing the readability level and general difficulty of textbooks. One tool often employed in the defense of education is the employment of readability indices in the evaluation of textbooks. There are a variety of these readability indices that serve the purpose of indicating a grade level for a particular piece of writing (Kinkaid, et. al., 1975). It's relatively easy to find dozens of sites where a teacher or interested person can submit text or a URL with the purpose of finding out the reading level expressed as a grade level for a particular piece of text. Most sites report on five different indices: Automated Readability Index, Flesch Reading Ease, Flesch-Kinkaid Score, Gunning-Fogg Index, and SMOG Index (Simplified Measure of Gobbledygook). This paper addresses these indices, their applications, and the drawbacks of their use..