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Personalized Intervention based on the Early Prediction of At-risk Students to Improve Their Learning Performance
Anna Y.Q. Huang
, Jei Wei Chang
, Albert C.M. Yang
, Hiroaki Ogata
, Shun Ting Li
, Ruo Xuan Yen
,
Stephen J.H. Yang
學習科技研究中心
資訊工程學系
研究成果
:
雜誌貢獻
›
期刊論文
›
同行評審
26
引文 斯高帕斯（Scopus）
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Keyphrases
Learning Performance
100%
Students At-risk
100%
Early Prediction
100%
Personalized Intervention
100%
Learning Activities
40%
Learning Strategies
40%
Traditional Class
40%
Review Activity
40%
Electronic Books
20%
Student Learning
20%
Student Learning Performance
20%
Northern Taiwan
20%
Artificial Intelligence
20%
Automatic Generation
20%
Automatically Generate
20%
Learning Styles
20%
Programming Learning
20%
Programming Knowledge
20%
Text Processing
20%
Machine Learning Techniques
20%
Self-regulated Learning
20%
Peer Learning
20%
Note-taking
20%
Student Notes
20%
Student Performance
20%
Learning Analysis
20%
First Use
20%
Higher Learning
20%
Help-seeking
20%
Artificial Intelligence Technology
20%
Python Programming
20%
Performance Prediction Model
20%
Learning Performance Prediction
20%
Metacognitive Self-regulation
20%
Bidirectional Encoder Representations from Transformers
20%
Transformer-2
20%
Learning Intervention
20%
Python Course
20%
Generative Transformer
20%
Effort Regulation
20%
Intervention Review
20%
Social Sciences
Learning Performance
100%
At-Risk Students
100%
Student Learning
33%
Learning Strategy
33%
Artificial Intelligence
33%
Learning Experiences
33%
Learning Method
16%
Self-Regulated Learning
16%
Legislation
16%
Peer Learning
16%
Critical Thinking
16%
Metacognitive Self-Regulation
16%
Bidirectional Encoder Representations From Transformers
16%
Text Processing
16%
Psychology
At-Risk Student
100%
Artificial Intelligence
100%
Critical Thinking
50%
Self-Regulation
50%
Text Processing
50%
Chemical Engineering
Artificial Intelligence
100%
Learning System
50%