Around 10% of the people have dyslexia, a neurological disability that impairs a person's ability to read and write. There is evidence that the presentation of the text has a significant effect on a text's accessibility for people with dyslexia. However, to the best of our knowledge, there are no experiments that objectively measure the impact of the font type on reading performance. In this paper, we present the first experiment that uses eye-tracking to measure the effect of font type on reading speed. Using a within-subject design, 48 subjects with dyslexia read 12 texts with 12 different fonts. Sans serif, monospaced and roman font styles significantly improved the reading performance over serif, proportional and italic fonts. On the basis of our results, we present a set of more accessible fonts for people with dyslexia.

Abstract. Around 10% of the population has dyslexia, a reading disability that negatively affects a person's ability to read and comprehend texts. Previous work has studied how to optimize the text layout, but adapting the text content has not received that much attention. In this paper, we present an eye-tracking study that investigates if people with dyslexia would benefit from content simplification. In an experiment with 46 people, 23 with dyslexia and 23 as a control group, we compare texts where words were substituted by shorter/longer and more/less frequent synonyms. Using more frequent words caused the participants with dyslexia to read significantly faster, while the use of shorter words caused them to understand the text better. Amongst the control group, no significant effects were found. These results provide evidence that people with dyslexia may benefit from interactive tools that perform lexical simplification.

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Worldwide, around 10% of the population has dyslexia, a specific learning disorder. Most of previous eye tracking ex periments with people with and without dyslexia have found differences between populations suggesting that eye move ments reflect the difficulties of individuals with dyslexia. In this paper, we present the first statistical model to predict readers with and without dyslexia using eye tracking mea sures. The model is trained and evaluated in a 10-fold cross experiment with a dataset composed of 1,135 readings of people with and without dyslexia that were recorded with an eye tracker. Our model, based on a Support Vector Ma chine binary classifier, reaches 80.18% accuracy using the most informative features. To the best of our knowledge, this is the first time that eye tracking measures are used to predict automatically readers with dyslexia using machine learning.

The presentation of a text has a significant effect on the reading speed of people with dyslexia. This paper presents a set of recommendations to customize texts on a computer screen in a more accessible way for this target group. This set is based in an eye tracking study with 92 people, 46 with dyslexia and 46 as control group, where the reading performance of the participants was measured. The following parameters were studied: color combinations for the font and the screen background, font size, column width as well as character, line and paragraph spacing. It was found that larger text and larger character spacing lead the participants with and without dyslexia to read significantly faster. The study is complemented with questionnaires to obtain the participants preferences for each of these parameters, finding other significant effects. These results provide evidence that people with dyslexia may benefit from specific text presentation parameters that make text on a screen more readable. So far, these recommendations based on eye tracking data are the most complete for people with dyslexia.

Mitkov and Ha (2003) and Mitkov et al. (2006) offered an alternative to the lengthy and demanding activity of developing multiple-choice test items by proposing an NLP-based methodology for construction of test items from instructive texts such as textbook chapters and encyclopaedia entries. One of the interesting research questions which emerged during these projects was how better quality distractors could automatically be chosen. This paper reports the results of a study seeking to establish which similarity measures generate better quality distractors of multiple-choice tests. Similarity measures employed in the procedure of selection of distractors are collocation patterns, four different methods of WordNetbased semantic similarity (extended gloss overlap measure, Leacock and Chodorow's, Jiang and Conrath's as well as Lin's measures), distributional similarity, phonetic similarity as well as a mixed strategy combining the aforementioned measures. The evaluation results show that the methods based on Lin's measure and on the mixed strategy outperform the rest, albeit not in a statistically significant fashion.

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Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified test and a predictive machine learning model. In a study with more than 3,600 participants, our model correctly detected over 80% of the participants with dyslexia. To check the robustness of the method we tested our method using a new data set with over 1,300 participants with age customized tests in a different environment -a tablet instead of a desktop computer- reaching a recall of over 78% for the class with dyslexia for children 12 years old or older. Our work shows that dyslexia can be screened using a machine learning approach. An online screening tool in Spanish based on our methods has already been used by more than 200,000 people.