Abstract
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.
Original language | English |
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Title of host publication | 29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings |
Editors | Maria Mercedes T. Rodrigo, Sridhar Iyer, Antonija Mitrovic, Hercy N. H. Cheng, Dan Kohen-Vacs, Camillia Matuk, Agnieszka Palalas, Ramkumar Rajenran, Kazuhisa Seta, Jingyun Wang |
Publisher | Asia-Pacific Society for Computers in Education |
Pages | 51-56 |
Number of pages | 6 |
ISBN (Electronic) | 9789869721479 |
Publication status | Published - 22 Nov 2021 |
Event | 29th International Conference on Computers in Education Conference, ICCE 2021 - Virtual, Online Duration: 22 Nov 2021 → 26 Nov 2021 |
Publication series
Name | 29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings |
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Volume | 1 |
Conference
Conference | 29th International Conference on Computers in Education Conference, ICCE 2021 |
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City | Virtual, Online |
Period | 22/11/21 → 26/11/21 |
Bibliographical note
Publisher Copyright:© 2021 29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings. All rights reserved
Keywords
- Filipino
- linguistic features
- natural language processing
- Readability assessment
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Education