Year: 2021
Type: preprint
Abstract: In order to ensure quality and effective learning, fluency, and comprehension, the proper identification of the difficulty levels of reading materials should be observed. In this paper, we describe the development of automatic machine learning-based readability assessment models for educational Filipino texts using the most diverse set of linguistic features for the language. Results show that using a Random Forest model obtained a high performance of 62.7% in terms of accuracy, and 66.1% when using the optimal combination of feature sets consisting of traditional and syllable pattern-based predictors.
Source: arXiv (Cornell University)
Authors: Imperial, Joseph Marvin, Ong, Ethel
Institution: De La Salle University
Language: English
Subfield: Linguistics and Language
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W3192697883