@inproceedings{013567d8bd8f4a49b644afe1ec19b0f3,
title = "Investigating Demographics and Behavioral Engagement Associated with Online Learning Performance",
abstract = "In recent years, online learning has become a viable alternative for learners worldwide to pursue higher education and gain advanced technical skills. In this work, we focused on data analysis to scrutinize the features associated with online learning performance and course selection. In particular, we investigated and compared how student demographic characteristics and behavioral engagement associated with academic performance based on a publicly accessible Open University Learning Analytics dataset (OULAD). We find that neighborhood poverty level, education background, active learning days and interaction times are positively associated with final learning results. In addition, students with different genders had bias in online course selection, where female students tended to favor social science courses and male had a preference for STEM. Students who performed well mainly came from learners with a well-educated prior background.",
keywords = "Educational Data Analysis, OULAD dataset, Online Learning Performance, Virtual Learning Environment",
author = "Yicong Liang and Di Zou and Wang, {Fu Lee} and Haoran Xie and Cheung, {Simon K.S.}",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 16th International Conference on Blended Learning, ICBL 2023 ; Conference date: 17-07-2023 Through 20-07-2023",
year = "2023",
doi = "10.1007/978-3-031-35731-2_12",
language = "English",
isbn = "9783031357305",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "124--136",
editor = "Chen Li and Cheung, {Simon K. S.} and Wang, {Fu Lee} and Angel Lu and Kwok, {Lam For}",
booktitle = "Blended Learning",
}