Publication Date
In 2025 | 0 |
Since 2024 | 2 |
Since 2021 (last 5 years) | 4 |
Since 2016 (last 10 years) | 4 |
Since 2006 (last 20 years) | 4 |
Descriptor
College Students | 4 |
Learning Analytics | 4 |
Prediction | 3 |
Accuracy | 2 |
Electronic Learning | 2 |
Foreign Countries | 2 |
Learning Processes | 2 |
Models | 2 |
Academic Achievement | 1 |
Algorithms | 1 |
At Risk Students | 1 |
More ▼ |
Source
IEEE Transactions on Learning… | 4 |
Author
Bo Zhang | 1 |
Chae, Younsoo | 1 |
Deho, Oscar Blessed | 1 |
Haibin Zhu | 1 |
Hua Ma | 1 |
Im, Chang-Hwan | 1 |
Joksimovic, Srecko | 1 |
Keqin Li | 1 |
Kim, Hodam | 1 |
Kim, Suhye | 1 |
Li, Jiuyong | 1 |
More ▼ |
Publication Type
Journal Articles | 4 |
Reports - Research | 2 |
Reports - Descriptive | 1 |
Reports - Evaluative | 1 |
Education Level
Higher Education | 4 |
Postsecondary Education | 4 |
Audience
Location
Australia | 1 |
China (Shanghai) | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Qin Ni; Yifei Mi; Yonghe Wu; Liang He; Yuhui Xu; Bo Zhang – IEEE Transactions on Learning Technologies, 2024
Learning style recognition is an indispensable part of achieving personalized learning in online learning systems. The traditional inventory method for learning style identification faces the limitations such as subject and static characteristics. Therefore, an automatic and reliable learning style recognition mechanism is designed in this…
Descriptors: Cognitive Style, Electronic Learning, Prediction, Identification
Deho, Oscar Blessed; Joksimovic, Srecko; Li, Jiuyong; Zhan, Chen; Liu, Jixue; Liu, Lin – IEEE Transactions on Learning Technologies, 2023
Many educational institutions are using predictive models to leverage actionable insights using student data and drive student success. A common task has been predicting students at risk of dropping out for the necessary interventions to be made. However, issues of discrimination by these predictive models based on protected attributes of students…
Descriptors: Learning Analytics, Models, Student Records, Prediction
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
Kim, Hodam; Chae, Younsoo; Kim, Suhye; Im, Chang-Hwan – IEEE Transactions on Learning Technologies, 2023
Owing to the rapid development of information and communication technologies, online or mobile learning content is widely available on the Internet. Unlike traditional face-to-face learning, online learning exhibits a critical limitation: real-time interactions between learners and teachers are generally not feasible in online learning. To…
Descriptors: College Students, Control Groups, Attention, Comprehension