Computational Text Analysis and Reading Comprehension Exam Complexity

This book offers a detailed analysis of text difficulty for B2 and C1 English reading. It introduces tools for automatic classification and understanding of comprehension challenges.

In Computational Text Analysis and Reading Comprehension Exam Complexity, the author presents a comprehensive exploration of the linguistic characteristics that define reading texts at the Intermediate (B2) and Advanced (C1) levels. This research culminates in the creation of a Text Classification Profile, which offers both qualitative and quantitative insights into the features that influence text difficulty. The study is particularly relevant for those preparing for the Greek State Certificate of English Language Proficiency exams, as it delineates the specific linguistic traits that characterize reading materials at these competency levels.

Furthermore, the book introduces the L.A.S.T. Text Difficulty Index, a groundbreaking tool that facilitates the automatic classification of reading texts according to their complexity. By analyzing a variety of texts, the author aims to provide a reliable method for estimating text difficulty, ensuring that educators and learners can better understand the challenges posed by different reading materials. The emphasis on reader variables adds an important dimension, as it investigates how individual differences among test takers may impact their perceptions of reading comprehension difficulty.

Ultimately, Computational Text Analysis and Reading Comprehension Exam Complexity serves as a valuable resource for educators, researchers, and students alike. It not only provides a framework for understanding text difficulty but also equips users with practical tools for improving reading comprehension strategies tailored to specific learner needs. The insights gained from this research can significantly enhance the preparation process for English language proficiency exams.