Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 4 |
| Since 2017 (last 10 years) | 4 |
| Since 2007 (last 20 years) | 4 |
Descriptor
| Algorithms | 4 |
| Electronic Learning | 4 |
| Prediction | 3 |
| Artificial Intelligence | 2 |
| Knowledge Level | 2 |
| Performance | 2 |
| Accuracy | 1 |
| Ambiguity (Semantics) | 1 |
| Assignments | 1 |
| Audio Equipment | 1 |
| Automation | 1 |
| More ▼ | |
Source
| IEEE Transactions on Learning… | 4 |
Author
| Bergamin, Per | 1 |
| Comsa, Ioan-Sorin | 1 |
| Enhong Chen | 1 |
| Hlosta, Martin | 1 |
| Huang, Zhuoxuan | 1 |
| Imhof, Christof | 1 |
| Li, Jingze | 1 |
| Ling Chen | 1 |
| Linyu Deng | 1 |
| Lishan Zhang | 1 |
| Ma, Hua | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 4 |
| Reports - Evaluative | 2 |
| Reports - Research | 2 |
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
Lishan Zhang; Linyu Deng; Sixv Zhang; Ling Chen – IEEE Transactions on Learning Technologies, 2024
With the popularity of online one-to-one tutoring, there are emerging concerns about the quality and effectiveness of this kind of tutoring. Although there are some evaluation methods available, they are heavily relied on manual coding by experts, which is too costly. Therefore, using machine learning to predict instruction quality automatically…
Descriptors: Automation, Classification, Artificial Intelligence, Tutoring
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
Imhof, Christof; Comsa, Ioan-Sorin; Hlosta, Martin; Parsaeifard, Behnam; Moser, Ivan; Bergamin, Per – IEEE Transactions on Learning Technologies, 2023
Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include a higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems (LMS) and learning analytics (LA), indicators of such behavior can be detected, enabling predictions of…
Descriptors: Prediction, Time Management, Electronic Learning, Artificial Intelligence

Peer reviewed
Direct link
