Estimating readability with the Strathclyde readability measure

Despite their significant limitations, readability measures that are easy to apply have definite appeal. With this in mind, we have been exploring the prospects for more insightful measures that are computer-based and, thereby, still easily applied. The orthodox reliance on intrinsic syntactic features is an inherent limitation of most readability measures, since they have no reference to the likelihood that readers will be acquainted with the constituent words and phrases. To accommodate this feature of 'human familiarity', we have devised a metric that combines traditional factors, such as Average Sentence Length, with a measure of word 'commonality' based upon word frequency. This paper details the derivation, nature and application of the Strathclyde Readability Measure (SRM)

Readability of texts in secondary school mathematics course books

This study aimed to investigate the readability of the fifth-, sixth, seventh and eighth-grade mathematics course books prepared in deference to the 2017 curriculum and distributed to schools by MEB. This study utilized the descriptive document analysis which is a qualitative research method. Readability of the mathematics course books were subjected to a quantitative analysis by Çetinkaya-Uzun Readability Formula. Addressing the readability levels of the texts in secondary mathematics course books, this study performed analyses of average word and average sentence lengths of the texts in secondary school course books. These analyses showed that there is no linear correlation between grade level and word and sentence length averages. Readability scores and levels of the text in the analyzed secondary school mathematics course books are not in parallel with the grade level. Accordingly, readability scores of information and solution texts in the fifth-grade course book were lower than the scores in other grades' course books whereas readability scores of question texts were lower than the scores in all grades' course books but the eighth-grade course book. Readability levels of the text in the analyzed secondary school mathematics course book were found to be on frustration level and educational level. Course books should also include independent texts with readability scores and level

All mixed up? Finding the optimal feature set for general readability prediction and its application to English and Dutch

Readability research has a long and rich tradition, but there has been too little focus on general readability prediction without targeting a specific audience or text genre. Moreover, though NLP-inspired research has focused on adding more complex readability features there is still no consensus on which features contribute most to the prediction. In this article, we investigate in close detail the feasibility of constructing a readability prediction system for English and Dutch generic text using supervised machine learning. Based on readability assessments by both experts and a crowd, we implement different types of text characteristics ranging from easy-to-compute superficial text characteristics to features requiring a deep linguistic processing, resulting in ten different feature groups. Both a regression and classification setup are investigated reflecting the two possible readability prediction tasks: scoring individual texts or comparing two texts. We show that going beyond correlation calculations for readability optimization using a wrapper-based genetic algorithm optimization approach is a promising task which provides considerable insights in which feature combinations contribute to the overall readability prediction. Since we also have gold standard information available for those features requiring deep processing we are able to investigate the true upper bound of our Dutch system. Interestingly, we will observe that the performance of our fully-automatic readability prediction pipeline is on par with the pipeline using golden deep syntactic and semantic information

A posteriori agreement as a quality measure for readability prediction systems

All readability research is ultimately concerned with the research question whether it is possible for a prediction system to automatically determine the level of readability of an unseen text. A significant problem for such a system is that readability might depend in part on the reader. If different readers assess the readability of texts in fundamentally different ways, there is insufficient a priori agreement to justify the correctness of a readability prediction system based on the texts assessed by those readers. We built a data set of readability assessments by expert readers. We clustered the experts into groups with greater a priori agreement and then measured for each group whether classifiers trained only on data from this group exhibited a classification bias. As this was found to be the case, the classification mechanism cannot be unproblematically generalized to a different user group

Readability, presentation and quality of allergy-related patient information leaflets: a cross sectional and longitudinal study

Objective: Patient information leaflets (PILs) are widely used to reinforce or illustrate health information and to complement verbal consultation. The objectives of the study were to assess the readability and presentation of PILs published by Allergy UK, and to conduct a longitudinal assessment to evaluate the impact of leaflet amendment and revision on readability. Methods: Readability of Allergy UK leaflets available in 2013 was assessed using Simple Measure of Gobbledegook (SMOG) and Flesch-Kincaid Reading Grade Formula. Leaflet presentation was evaluated using the Clear Print Guidelines of the Royal National Institute of Blind People (RNIB) and the Patient Information Appraisal System developed by the British Medical Association (BMA). Changes in the leaflets’ readability scores over five years were investigated. Results: 108 leaflets, covering a wide range of allergic conditions and treatment options, were assessed. The leaflets had average SMOG and Flesch-Kincaid scores of 13.9 (range 11-18, SD 1.2) and 10.9 (range 5-17, SD 2.1) respectively. All leaflets met the RNIB Clear Print guidelines, with the exception of font size which was universally inadequate. The leaflets scored on average 10 (median 10, range 7-15) out of a maximum of 27 on the BMA checklist. The overall average SMOG score of 31 leaflets available in both 2008 and 2013 had not changed significantly. The process of leaflet revision resulted in 1% change in readability scores overall, with a predominantly upward trend with six leaflets increasing their readability score by >10% and only three decreasing by >10%. Conclusion: Allergy-related patient information leaflets are well presented but have readability levels that are higher than those recommended for health information. Involving service users in the process of leaflet design, together with systematic pre-publication screening of readability would enhance the accessibility and comprehensibility of written information for people with allergy and their careers

Improving legibility of natural deduction proofs is not trivial

In formal proof checking environments such as Mizar it is not merely the validity of mathematical formulas that is evaluated in the process of adoption to the body of accepted formalizations, but also the readability of the proofs that witness validity. As in case of computer programs, such proof scripts may sometimes be more and sometimes be less readable. To better understand the notion of readability of formal proofs, and to assess and improve their readability, we propose in this paper a method of improving proof readability based on Behaghel's First Law of sentence structure. Our method maximizes the number of local references to the directly preceding statement in a proof linearisation. It is shown that our optimization method is NP-complete.Comment: 33 page