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In order to ensure quality and effective learning, fluency, andcomprehension, the proper identification of the difficulty levels of readingmaterials should be observed. In this paper, we describe the development ofautomatic machine learning-based readability assessment models for educationalFilipino texts using the most diverse set of linguistic features for thelanguage. Results show that using a Random Forest model obtained a highperformance of 62.7% in terms of accuracy, and 66.1% when using the optimalcombination of feature sets consisting of traditional and syllablepattern-based predictors.

Diverse Linguistic Features for Assessing Reading Difficulty of Educational Filipino Texts