"Predicting Online English Language Learning Outcome from Learner-, Instructor-, and Course-Level Factors"

​AERA 2018 Annual Meeting: “The Dreams, Possibilities, and Necessity of Public Education”
April 13-17, 2018
New York

Title: "Predicting Online English Language Learning Outcome from Learner-, Instructor-, and Course-Level Factors"
Authors: Binbin Zheng (PhD Alumna), Chin-Hsi Lin (PhD Alumnus), Jemma Bae Kwon

Abstract

​Online language learning poses great challenges on both learners and instructors. While most studies of online language learning used students’ self-perceived data and focused on higher education, this study provides a comprehensive examination of factors predicting online high-school English language learning outcome from learner, instructor, and course level using data from the learning management system (LMS). Using hierarchical linear modeling with 919 student data nested in 8 course courses taught by 13 instructors, our findings suggested that students’ login times and durations in the LMS significantly predicts their academic achievement. Furthermore, having more project-based assignments and text resources such as instructor guide helped enhance student learning. Implications for online language course design, teaching practices, and research are discussed.