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Hua Ma; Wen Zhao; Yuqi Tang; Peiji Huang; Haibin Zhu; Wensheng Tang; Keqin Li – IEEE Transactions on Learning Technologies, 2024
To prevent students from learning risks and improve teachers' teaching quality, it is of great significance to provide accurate early warning of learning performance to students by analyzing their interactions through an e-learning system. In existing research, the correlations between learning risks and students' changing cognitive abilities or…
Descriptors: College Students, Learning Analytics, Learning Management Systems, Academic Achievement
Sonja Kleter; Uwe Matzat; Rianne Conijn – IEEE Transactions on Learning Technologies, 2024
Much of learning analytics research has focused on factors influencing model generalizability of predictive models for academic performance. The degree of model generalizability across courses may depend on aspects, such as the similarity of the course setup, course material, the student cohort, or the teacher. Which of these contextual factors…
Descriptors: Prediction, Models, Academic Achievement, Learning Analytics
Vo, Thi Ngoc Chau; Nguyen, Phung – IEEE Transactions on Learning Technologies, 2021
A course-level early final study status prediction task is to predict as soon as possible the final success of each student after studying a course. It is significant because each successful course accomplishment is required for a degree. Further, early predictions provide enough time to make necessary changes for ultimate success. This article…
Descriptors: Prediction, Academic Achievement, Data Collection, Learning Processes
Xiuyu Lin; Zehui Zhan; Xuebo Zhang; Jiayi Xiong – IEEE Transactions on Learning Technologies, 2024
The attribution of learning success or failure is crucial for students' learning and motivation. Effective attribution of their learning success or failure in the context of a small private online course (SPOC) could generate students' motivation toward learning success while an incorrect attribution would lead to a sense of helplessness. Based on…
Descriptors: Learning Analytics, Learning Processes, Learning Motivation, Attribution Theory
Saint, John; Whitelock-Wainwright, Alexander; Gasevic, Dragan; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2020
The recent focus on learning analytics (LA) to analyze temporal dimensions of learning holds the promise of providing insights into latent constructs, such as learning strategy, self-regulated learning (SRL), and metacognition. These methods seek to provide an enriched view of learner behaviors beyond the scope of commonly used correlational or…
Descriptors: Undergraduate Students, Engineering Education, Learning Analytics, Learning Strategies
Meng, Lingling; Zhang, Wanxue; Chu, Yu; Zhang, Mingxin – IEEE Transactions on Learning Technologies, 2021
With the rapid advancement of education, personalized learning has gained considerable attention in recent years. Learning path plays an important role in this area and has attracted great concern. Many generating mechanisms have been proposed from different perspectives for assisting learning. Some methods focus on learners' interest, while some…
Descriptors: Educational Diagnosis, Individualized Instruction, Learning Processes, Cognitive Ability
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
Tempelaar, Dirk T.; Rienties, Bart; Nguyen, Quan – IEEE Transactions on Learning Technologies, 2017
Studies in the field of learning analytics (LA) have shown students' demographics and learning management system (LMS) data to be effective identifiers of "at risk" performance. However, insights generated by these predictive models may not be suitable for pedagogically informed interventions due to the inability to explain why students…
Descriptors: Student Behavior, Integrated Learning Systems, Personality, Educational Research