Predicting the Easiness and Complexity of English Health Materials for International Tertiary Students With Linguistically Enhanced Machine Learning Algorithms: Development and Validation Study.

ABSTRACT:

Background

There is an increasing body of research on the development of machine learning algorithms in the evaluation of online health educational resources for specific readerships. Machine learning algorithms are known for their lack of interpretability compared with statistics. Given their high predictive precision, improving the interpretability of these algorithms can help increase their applicability and replicability in health educational research and applied linguistics, as well as in the development and review of new health education resources for effective and accessible health education.

Objective

Our study aimed to develop a linguistically enriched machine learning model to predict binary outcomes of online English health educational resources in terms of their

SUBMITTER: Xie W 

PROVIDER: S-EPMC8579219 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature