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Li, Jiawei; Supraja, S.; Qiu, Wei; Khong, Andy W. H. – International Educational Data Mining Society, 2022
Academic grades in assessments are predicted to determine if a student is at risk of failing a course. Sequential models or graph neural networks that have been employed for grade prediction do not consider relationships between course descriptions. We propose the use of text mining to extract semantic, syntactic, and frequency-based features from…
Descriptors: Course Descriptions, Learning Analytics, Academic Achievement, Prediction
Wan, Han; Zhong, Zihao; Tang, Lina; Gao, Xiaopeng – IEEE Transactions on Learning Technologies, 2023
Small private online courses (SPOCs) have influenced teaching and learning in China's higher education. Learning management systems (LMSs) are important components in SPOCs. They can collect various data related to student behavior and support pedagogical interventions. This research used feature engineering and nearest neighbor smoothing models…
Descriptors: Online Courses, Learning Management Systems, Higher Education, Student Behavior
Tatel, Corey E.; Lyndgaard, Sibley F.; Kanfer, Ruth; Melkers, Julia E. – Journal of Learning Analytics, 2022
As the demand for lifelong learning increases, many working adults have turned to online graduate education in order to update their skillsets and pursue advanced credentials. Simultaneously, the volume of data available to educators and scholars interested in online learning continues to rise. This study seeks to extend learning analytics…
Descriptors: Course Selection (Students), Enrollment Trends, Academic Achievement, Learning Analytics