Readability formulas: An analysis into reading index of prose forms

Text comprehension will suffer if the readability level is not accessible to the students. Readability formulas predict text complexity, assisting in appropriate text selection that complements students’ reading abilities to improve their language development. Therefore, the study aims to find out the reading index of the prose forms in the literature component catered to lower secondary school students ages 13 and 14 years old in Form One (seventh grade) and Form Two (eighth grade) classrooms in Malaysia. The reading index is measured by using four readability formulas which are Dale-Chall, Fog, SMOG, and Flesch-Kincaid that focuses on the words, sentences, syllables, and polysyllable words. These formulas are used to predict the level of difficulty of the prose forms. The reading index calculated from these readability formulas reveals the grade level of the prose forms. The grade level indicates the best age for reading and understanding the prose forms. Two prose forms were chosen as samples in the study. A passage is chosen from each prose form to be uploaded using the online tool. The indices obtained from the readability formulas predicted that both of the prose forms were below students’ reading age. The study implicates reading index must be taken into consideration in literary texts selection because it is an indicator of the years of education that an individual requires to comprehend the literary text clearly. Suitable reading material at students’ age level can enhance literature learning and teaching in the ESL classroom.

Research on the readability of the chosen General Insurance Terms and Conditions on the Polish market

Readability and the study of complexity of functional texts are of interest to many contemporary researchers. Nowadays, in the age of miniaturization, simplification of our everyday life components, portability, and domination of short text forms in the mass media, we can observe a decrease in interest in reading larger texts or those containing specialized, difficult to understand vocabulary. This is how consumer contracts (including insurance contracts) are perceived. The purpose of this article is, therefore, to examine and evaluate the readability of the General Insurance Terms and Conditions (hereinafter referred as: Insurance GTC) — an integral part of the insurance contract. In order to determine whether the language of the Insurance GTC is easy to understand, precise and clear for consumers, a quantitative and qualitative analysis was carried out using readability formulas, in particular FOG-PL — fully adapted to the specificity of the Polish language. The Insurance GTC corpus from four different insurance groups was examined, and its level of language “fogginess” was compared with other utility texts.

Combining Insights of Medical Readability Tools and Machine Learning for Reader-oriented Health Resource Evaluation (Preprint)

BACKGROUND Medical texts on the websites are rich resources for the general public to access health information and get advice to assist them with their health concerns. However, the reading comprehension required for this type of information is far more complex than just reading the text alone, because it often requires a high health knowledge or health literacy in the domain-specific disease area. Furthermore, the reading ability of an individual is also influenced by others factors such as literacy, age, morbidities, social-economic status, interest in a specific health topic, cultural and linguistic background. Literature suggests that traditional readability formulas were designed to give one score for all readers. This inevitably urges for a more adaptive readability assessment tools to evaluate online medical information for people with various backgrounds in a much more comprehensive way. OBJECTIVE The aim of this study was to clarify the existing controversy around the inconsistency among readability formulas, and to build a reader-oriented readability assessment tool, which could automatically estimate the readability of online health information in considering the diverse backgrounds from readers. METHODS The aim of this study was to clarify the existing controversy around the inconsistency among readability formulas, and to build a reader-oriented readability assessment tool, which could automatically estimate the readability of online health information in considering the diverse backgrounds from readers. RESULTS We found that the machine learning readability models integrating multiple readability formulas were more effective to estimate readability of online infectious disease information than the individual readability formula alone. The integrated machine-learning models incorporated the features from the readability formulas, while considered specific backgrounds of readers, which resulted in a more superior performance in the readability classification. CONCLUSIONS The empirical study combined with the existing readability formulas and the machine-learning techniques resulted in more accurate prediction of reading difficulties extended beyond the linguistic features originated from the readability formulas. The proposed assessment tool provides a reader-oriented assessment to be more effective in proxy the health information readability. The key significance of the study includes its reader centeredness, which incorporated the diverse backgrounds from the readers, and its clarification of the relative effectiveness and compatibility of different medical readability tools via machine learning.

Predicting Slovene Text Complexity Using Readability Measures

The majority of existing readability measures are designed for English texts. We aim to adapt and test the readability measures on Slovene. We test ten well-known readability formulas and eight additional readability criteria on five types of texts: children’s magazines, general magazines, daily newspapers, technical magazines, and transcriptions of national assembly sessions. As these groups of texts target different audiences, we assume that the differences in writing styles should be reflected in their readability scores. Our analysis shows which readability measures perform well on this task and which fail to distinguish between the groups.

Text Readability: A Snapshot

Selecting suitable reading materials are taxing and challenging for many English instructors. Text readability analysis can be used to automate the process of reading material selection and also the assessment of reading ability for language learners. Readability formulas have been broadly used in determining text difficulty based on learners’ grade level. Based on mathematical calculations, a readability formula examines certain features of a text in order to provide best rough approximations as an indication of difficulty. This paper reflects some aspects and issues of readability analysis.

READABILITY OF TEXTBOOK VS LINUS SCREENING TEXT: IMPLICATION ON YEAR 4 POST LINUS PUPILS

Reading is one of the most important skills that need to be acquired. Due to this the Malaysian government implemented the LINUS programme in school to help pupils master the basic skill involved English. The problems for most LINUS pupils come after they pass their LINUS screening. They were not able to read most materials in the text and read with difficulty. It is hoped that by finding the readability level of the texts in the textbook and the LINUS screening it will shed a light on how teacher could handle the problems. For this study 6 texts; 3 from each the Textbook and LINUS screening respectively was chosen randomly. The texts were calculated using the SMOG, FOG, Flesch-Kincaid and Spache formula to obtain their readability level. Spearman correlation test was conducted to see the consistency between the readability formulas in predicting the difficulty level. It is found that the texts in the textbook is more difficult compared to the LINUS texts.