Shukai Duan
Biographic Data
| ID | 5378607 |
|---|---|
| NAME | Shukai Duan |
| GIVEN NAMES | Shukai |
| FAMILY NAME | Duan |
| SIGNATURE | DUAN S |
| AFFILIATIONS | Southwest University |
| ORCID | 0000-0002-0040-3796 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Integrating Computational Thinking into K-12 Artificial Intelligence Education
Amid the rapid advancement of artificial intelligence (AI) and the digital transformation of schooling, computational thinking has become a foundational competency for K-12 learners and an organizing principle for AI education. Existing research suggests that students need not only programming knowledge, but also the ability to analyze problems, reason with data and models, and evaluate intelligent systems responsibly. However, current K-12 AI ed…
Stage-Dependent Behavioral Patterns in Mooc Dropout: An Explainable Learning Analytics Study
The high dropout rate in massive open online courses (MOOCs) continues to limit their potential in promoting inclusive and sustainable learning. Although many prediction models have been used to identify potential dropouts, most studies view dropout as a static classification problem and fail to clearly reveal the dynamic trajectory of learner participation over time. Therefore, this study introduces a phased analysis perspective, treating MOOC d…
Resting-state functional connectome predicts individual differences in depression during Covid-19 pandemic
Stressful life events are significant risk factors for depression, and increases in depressive symptoms have been observed during the COVID-19 pandemic. The aim of this study is to explore the neural makers for individuals' depression during COVID-19, using connectome-based predictive modeling (CPM). Then we tested whether these neural markers could be used to identify groups at high/low risk for depression with a longitudinal dataset. The result…
No prominent works on this page.
Resting-state functional connectome predicts individual differences in depression during Covid-19 pandemic
Stressful life events are significant risk factors for depression, and increases in depressive symptoms have been observed during the COVID-19 pandemic. The aim of this study is to explore the neural makers for individuals' depression during COVID-19, using connectome-based predictive modeling (CPM). Then we tested whether these neural markers could be used to identify groups at high/low risk for depression with a longitudinal dataset. The result…
Integrating Computational Thinking into K-12 Artificial Intelligence Education
Amid the rapid advancement of artificial intelligence (AI) and the digital transformation of schooling, computational thinking has become a foundational competency for K-12 learners and an organizing principle for AI education. Existing research suggests that students need not only programming knowledge, but also the ability to analyze problems, reason with data and models, and evaluate intelligent systems responsibly. However, current K-12 AI ed…
Stage-Dependent Behavioral Patterns in Mooc Dropout: An Explainable Learning Analytics Study
The high dropout rate in massive open online courses (MOOCs) continues to limit their potential in promoting inclusive and sustainable learning. Although many prediction models have been used to identify potential dropouts, most studies view dropout as a static classification problem and fail to clearly reveal the dynamic trajectory of learner participation over time. Therefore, this study introduces a phased analysis perspective, treating MOOC d…
Intelligent Tutoring Systems and Adaptive Learning (2 works) · Online Learning and Analytics (2 works) · Teaching and Learning Programming (2 works) · Analytics (1 works) · Applications of artificial intelligence (1 works) · Behavioral pattern (1 works) · Biology (1 works) · Clinical Psychology (1 works) · Cognition (1 works) · Computational intelligence (1 works)