Exploring Automatic Readability Assessment for Science Documents within a Multilingual Educational Context

This paper develops and evaluates automatic readability assessment models for science education texts in Basque, Spanish, and English at the secondary education level (ages 12-16), creating domain-specific corpora and testing both feature-based machine learning and deep learning approaches to help teachers find appropriate materials for multilingual STEM instruction.

Current student-centred, multilingual, active teaching methodologies require that teachers have continuous access to texts that are adequate in terms of topic and language competence. However, the task of finding appropriate materials is arduous and time consuming for teachers. To build on automatic readability assessment research that could help to assist teachers, we explore the performance of natural language processing approaches when dealing with educational science documents for secondary

Study Type

Source

Framework Categories

Tool Types

Tags