Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 3 |
| Since 2017 (last 10 years) | 3 |
| Since 2007 (last 20 years) | 3 |
Descriptor
| Algorithms | 3 |
| Knowledge Level | 3 |
| Electronic Learning | 2 |
| Prediction | 2 |
| Student Evaluation | 2 |
| Ambiguity (Semantics) | 1 |
| Attribution Theory | 1 |
| Cognitive Measurement | 1 |
| Correlation | 1 |
| Data Analysis | 1 |
| Educational Assessment | 1 |
| More ▼ | |
Source
| IEEE Transactions on Learning… | 3 |
Author
| Changqin Huang | 1 |
| Enhong Chen | 1 |
| Huang, Zhuoxuan | 1 |
| Jia Zhu | 1 |
| Li, Jingze | 1 |
| Ma, Hua | 1 |
| Minghao Yin | 1 |
| Minjuan Wang | 1 |
| Qi Liu | 1 |
| Shuanghong Shen | 1 |
| Tang, Wensheng | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 3 |
| Reports - Evaluative | 2 |
| Reports - Research | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Shuanghong Shen; Qi Liu; Zhenya Huang; Yonghe Zheng; Minghao Yin; Minjuan Wang; Enhong Chen – IEEE Transactions on Learning Technologies, 2024
Modern online education has the capacity to provide intelligent educational services by automatically analyzing substantial amounts of student behavioral data. Knowledge tracing (KT) is one of the fundamental tasks for student behavioral data analysis, aiming to monitor students' evolving knowledge state during their problem-solving process. In…
Descriptors: Student Behavior, Electronic Learning, Data Analysis, Models
Jia Zhu; Xiaodong Ma; Changqin Huang – IEEE Transactions on Learning Technologies, 2024
Knowledge tracing (KT) for evaluating students' knowledge is an essential task in personalized education. More and more researchers have devoted themselves to solving KT tasks, e.g., deep knowledge tracing (DKT), which can capture more sophisticated representations of student knowledge. Nonetheless, these techniques ignore the reconstruction of…
Descriptors: Teaching Methods, Knowledge Level, Algorithms, Attribution Theory
Ma, Hua; Huang, Zhuoxuan; Tang, Wensheng; Zhu, Haibin; Zhang, Hongyu; Li, Jingze – IEEE Transactions on Learning Technologies, 2023
To provide intelligent learning guidance for students in e-learning systems, it is necessary to accurately predict their performance in future exams by analyzing score data in past exams. However, existing research has not addressed the uncertain and dynamic features of students' cognitive status, whereas these features are essential for improving…
Descriptors: Prediction, Student Evaluation, Performance, Tests

Peer reviewed
Direct link
