Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 4 |
Since 2006 (last 20 years) | 4 |
Descriptor
Prior Learning | 4 |
Evaluation Methods | 3 |
Prediction | 3 |
Intelligent Tutoring Systems | 2 |
Learning Processes | 2 |
Models | 2 |
Performance | 2 |
Student Evaluation | 2 |
Teaching Methods | 2 |
Accuracy | 1 |
Artificial Intelligence | 1 |
More ▼ |
Source
IEEE Transactions on Learning… | 4 |
Author
Chen, Enhong | 1 |
Huang, Zhenya | 1 |
Jiang, Yuncheng | 1 |
Liu, Qi | 1 |
Ma, Jianhui | 1 |
Mao, Shun | 1 |
Martina Angela Rau | 1 |
Miyazawa, Yoshimitsu | 1 |
Sally Wu | 1 |
Ueno, Maomi | 1 |
Wang, Fei | 1 |
More ▼ |
Publication Type
Journal Articles | 4 |
Reports - Research | 4 |
Education Level
Higher Education | 2 |
Postsecondary Education | 2 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Mao, Shun; Zhan, Jieyu; Wang, Yizhao; Jiang, Yuncheng – IEEE Transactions on Learning Technologies, 2023
For offering adaptive learning to learners in intelligent tutoring systems, one of the fundamental tasks is knowledge tracing (KT), which aims to assess learners' learning states and make prediction for future performance. However, there are two crucial issues in deep learning-based KT models. First, the knowledge concepts are used to predict…
Descriptors: Intelligent Tutoring Systems, Learning Processes, Prediction, Prior Learning
Wang, Fei; Huang, Zhenya; Liu, Qi; Chen, Enhong; Yin, Yu; Ma, Jianhui; Wang, Shijin – IEEE Transactions on Learning Technologies, 2023
To provide personalized support on educational platforms, it is crucial to model the evolution of students' knowledge states. Knowledge tracing is one of the most popular technologies for this purpose, and deep learning-based methods have achieved state-of-the-art performance. Compared to classical models, such as Bayesian knowledge tracing, which…
Descriptors: Cognitive Measurement, Diagnostic Tests, Models, Prediction
Martina Angela Rau; Will Keesler; Ying Zhang; Sally Wu – IEEE Transactions on Learning Technologies, 2020
Instruction in most STEM domains uses visuals to illustrate complex problems. During problem solving, students often manipulate and construct visuals. Traditionally, students draw visuals on paper and receive delayed feedback from an instructor. Educational technologies have the advantage that they can provide immediate feedback on students'…
Descriptors: Visualization, Educational Technology, Chemistry, STEM Education
Ueno, Maomi; Miyazawa, Yoshimitsu – IEEE Transactions on Learning Technologies, 2018
Over the past few decades, many studies conducted in the field of learning science have described that scaffolding plays an important role in human learning. To scaffold a learner efficiently, a teacher should predict how much support a learner must have to complete tasks and then decide the optimal degree of assistance to support the learner's…
Descriptors: Scaffolding (Teaching Technique), Prediction, Probability, Comparative Analysis