Assessing English language sentences readability using machine learning models

Readability is an active field of research in the late nineteenth century and vigorously persuaded to date. The recent boom in data-driven machine learning has created a viable path forward for readability classification and ranking. The evaluation of text readability is a time-honoured issue with even more relevance in today’s information-rich world. This paper addresses the task of readability assessment for the English language. Given the input sentences, the objective is to predict its level of readability, which corresponds to the level of literacy anticipated from the target readers. This readability aspect plays a crucial role in drafting and comprehending processes of English language learning. Selecting and presenting a suitable collection of sentences for English Language Learners may play a vital role in enhancing their learning curve. In this research, we have used 30,000 English sentences for experimentation. Additionally, they have been annotated into seven different readability levels using Flesch Kincaid. Later, various experiments were conducted using five Machine Learning algorithms, i.e., KNN, SVM, LR, NB, and ANN. The classification models render excellent and stable results. The ANN model obtained an F-score of 0.95% on the test set. The developed model may be used in education setup for tasks such as language learning, assessing the reading and writing abilities of a learner.

Using a parallel corpus to adapt the Flesch Reading Ease formula to Czech

Text readability metrics assess how much effort a reader must put into comprehending a given text. They are, e.g., used to choose appropriate readings for different student proficiency levels, or to make sure that crucial information is efficiently conveyed (e.g., in an emergency). Flesch Reading Ease is such a globally used formula that it is even integrated into the MS Word Processor. However, its constants are language-dependent. The original formula was created for English. So far it has been adapted to several European languages, Bangla, and Hindi. This paper describes the Czech adaptation, with the language-dependent constants optimized by a machine-learning algorithm working on parallel corpora of Czech and English, Russian, Italian, and French, respectively.

Conversion Rate Prediction Based on Text Readability Analysis of Landing Pages

Digital marketing has been extensively researched and developed remarkably rapidly over the last decade. Within this field, hundreds of scientific publications and patents have been produced, but the accuracy of prediction technologies leaves much to be desired. Conversion prediction remains a problem for most marketing professionals. In this article, the authors, using a dataset containing landing pages content and their conversions, show that a detailed analysis of text readability is capable of predicting conversion rates. They identify specific features that directly affect conversion and show how marketing professionals can use the results of this work. In their experiments, the authors show that the applied machine learning approach can predict landing page conversion. They built five machine learning models. The accuracy of the built machine learning model using the SVM algorithm is promising for its implementation. Additionally, the interpretation of the results of this model was conducted using the SHAP package. Approximately 60% of purchases are made by nonmembers, and this paper may be suitable for the cold-start problem.

Cognitively Driven Arabic Text Readability Assessment Using Eye-Tracking

Using physiological data helps to identify the cognitive processing in the human brain. One method of obtaining these behavioral signals is by using eye-tracking technology. Previous cognitive psychology literature shows that readable and difficult-to-read texts are associated with certain eye movement patterns, which has recently encouraged researchers to use these patterns for readability assessment tasks. However, although it seems promising, this research direction has not been explored adequately, particularly for Arabic. The Arabic language is defined by its own rules and has its own characteristics and challenges. There is still a clear gap in determining the potential of using eye-tracking measures to improve Arabic text. Motivated by this, we present a pilot study to explore the extent to which eye-tracking measures enhance Arabic text readability. We collected the eye movements of 41 participants while reading Arabic texts to provide real-time processing of the text; these data were further analyzed and used to build several readability prediction models using different regression algorithms. The findings show an improvement in the readability prediction task, which requires further investigation. To the best of our knowledge, this work is the first study to explore the relationship between Arabic readability and eye movement patterns.

Text Readability Level Investigation about Biology Subjects-related Units in 'Science 7 Textbook'

This study investigates text readability level investigation about biology subjects-related units in 'Science 7 Textbook'. A document analysis method was used in this study based on a qualitative research approach. The textbook, which has been used since the 2019-2020 academic year, has been selected as the study material. In the textbook, a total of 15 texts were randomly determined from two units related to biology subjects, one for each of the learning outcomes specified in the 2018 Science Course Curriculum. In calculating the readability levels of the texts, the 'Ateşman Readability Formula' was used. The findings obtained as a result of the analysis showed that the overall readability levels of the texts in both units were in the 'medium difficulty' category. Furthermore, when the findings of each unit were examined on a subject basis, it was determined that the texts belonging to all of the topics included in both units (Cell and Divisions, Reproduction, Growth and Development in Living Beings) were found in the 'medium' category. These findings were interpreted as the science textbook prepared for the seventh grade for the student level in terms of readability.

Assessment and Evaluation on Text Readability in Reading Test Instrument Development for BIPA-1 to BIPA-3

Nowadays, text readability is of great importance. Simple but very often ignored, readability statistics can provide information about the level of difficulty of the readability of particular documents and increase an evaluator’s credibility. Hence, this research aims to examine the readability index of the test instrument for BIPA (Bahasa Indonesia Untuk Penutur Asing), evaluate the student's reading ability, and analyze the relevance readability index and cloze test result. The study was carried out through an experiment involving 21 international students in several Muhammadiyah Universities. The students were provided with three sets of reading instruments from BIPA-1 to BIPA-3. Data analysis was carried out with correlation analysis. The result showed that the reading instrument difficulty was at a fairly easy to moderate level. Gunning Fog score and Automated Readability Index were the most relevant tool to the student's test achievement. This research implies the importance of test instrument evaluation. Assessment of the readability of the text is vital in the process of developing an appropriate test instrument. Keywords: BIPA, cloze, difficulty, instrument, readability.

Investigating Indonesian Textbooks Readability Using Fry Graph Formula

Learning the Indonesian language cannot be separated from the use of textbooks. In using textbooks, teachers must consider several aspects, especially text readability. This study aims to measure text readability in Indonesian textbooks using the Fry Graph Formula and determine the suitability of the text used in the book with the student's school level. The present study applied the descriptive analysis and quantitative approach method to count and analyze texts in several Indonesian language textbooks readability using the Fry graph formula. Three Indonesian language textbooks with 45 measured text in 11th grade were analyzed. This study found that 25 texts showed the results "Not Appropriate", 16 texts showed the results "Appropriate", and four texts showed the results "Invalid". Based on the analysis, the number of readabilities of text in Indonesian language textbooks was easy for textbooks at 11th grade. The findings in this study can help the teacher adjust and choose the suitable teaching materials in Indonesian language textbook to improve teaching and learning quality. Before selecting a leading textbook as a learning source, we suggest teachers analyze text readability to improve learning efficiency and quality.