Zhu Su
Biographic Data
| ID | 9493923 |
|---|---|
| NAME | Zhu Su |
| GIVEN NAMES | Zhu |
| FAMILY NAME | Su |
| SIGNATURE | SU Z |
| AFFILIATIONS | Central China Normal University |
| ORCID | 0000-0002-4094-7261 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Network-based sentiment analysis
Sentiment analysis is crucial in education, yet traditional lexicon-based methods struggle to capture nuanced emotions and group dynamics. We propose a network-based approach using a dataset of 9,873 forum posts from 1,919 MOOC learners. In our sentiment network, learners are nodes in an emotional state space, connected when their emotional distance—defined as the Manhattan distance between their emotion-score vectors—is below a threshold. Analys…
Emotion dynamics in online learning communities
An analysis approach for blended learning based on weighted multiplex networks
Learners' interaction patterns in asynchronous online discussions
Studying the networked nature of social and cognitive aspects of learner interactions is the key to understanding how successful collaborative learning occurs in asynchronous online discussion forums (AODFs). Guided by network science and multiplex network analysis, this study compared the differences of network structure and properties between the social (learners as nodes and commenting on the others' contributions as edges) and cognitive (lear…
No prominent works on this page.
Learners' interaction patterns in asynchronous online discussions
Studying the networked nature of social and cognitive aspects of learner interactions is the key to understanding how successful collaborative learning occurs in asynchronous online discussion forums (AODFs). Guided by network science and multiplex network analysis, this study compared the differences of network structure and properties between the social (learners as nodes and commenting on the others' contributions as edges) and cognitive (lear…
An analysis approach for blended learning based on weighted multiplex networks
Network-based sentiment analysis
Sentiment analysis is crucial in education, yet traditional lexicon-based methods struggle to capture nuanced emotions and group dynamics. We propose a network-based approach using a dataset of 9,873 forum posts from 1,919 MOOC learners. In our sentiment network, learners are nodes in an emotional state space, connected when their emotional distance—defined as the Manhattan distance between their emotion-score vectors—is below a threshold. Analys…
Emotion dynamics in online learning communities
Cognition (2 works) · Computer Science (2 works) · Educational technology (2 works) · Innovative Teaching and Learning Methods (2 works) · Mathematics education (2 works) · Online and Blended Learning (2 works) · Online Learning and Analytics (2 works) · Psychology (2 works) · Artificial Intelligence (1 works) · Asynchronous communication (1 works)