Lingchen Kong
Biographic Data
| ID | 7948889 |
|---|---|
| NAME | Lingchen Kong |
| GIVEN NAMES | Lingchen |
| FAMILY NAME | Kong |
| SIGNATURE | KONG L |
| AFFILIATIONS | Beijing Jiaotong University |
| ORCID | 0000-0002-9168-6145 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Catalyst: Learner feedback mining with an LLM-based automated thematic analysis
Learner feedback offers critical insights for improving digital learning platforms, yet analyzing large volumes of unstructured qualitative data remains challenging. Traditional thematic analysis depends on manual coding, which is time-consuming, resource-intensive, and susceptible to human bias. This study introduces CATALYST (Cluster Augmented Thematic Analysis using Locally-deployed Yet Scalable Tool), an automated framework that integrates K-…
How Does Metro Maintenance Staff’s Risk Perception Influence Safety Citizenship Behavior—The Mediating Role of Safety Attitude
The accident rate is high in subway maintenance work, and most of the accidents are caused by human factors, especially the lack of sensitivity to risk perception, the lack of rigorous attitude towards safety and the lack of safe citizenship behavior (SCB). Therefore, it is very important to study the risk perception (RP), safety attitude (SA) and SCB of metro maintenance staff in order to reduce the accident rate. In order to reduce human errors…
No prominent works on this page.
How Does Metro Maintenance Staff’s Risk Perception Influence Safety Citizenship Behavior—The Mediating Role of Safety Attitude
The accident rate is high in subway maintenance work, and most of the accidents are caused by human factors, especially the lack of sensitivity to risk perception, the lack of rigorous attitude towards safety and the lack of safe citizenship behavior (SCB). Therefore, it is very important to study the risk perception (RP), safety attitude (SA) and SCB of metro maintenance staff in order to reduce the accident rate. In order to reduce human errors…
Catalyst: Learner feedback mining with an LLM-based automated thematic analysis
Learner feedback offers critical insights for improving digital learning platforms, yet analyzing large volumes of unstructured qualitative data remains challenging. Traditional thematic analysis depends on manual coding, which is time-consuming, resource-intensive, and susceptible to human bias. This study introduces CATALYST (Cluster Augmented Thematic Analysis using Locally-deployed Yet Scalable Tool), an automated framework that integrates K-…
Applied Psychology (1 works) · Applied Psychology (1 works) · Business (1 works) · Citizenship (1 works) · Computer-Assisted Instruction (1 works) · Educational technology (1 works) · Electronic learning (1 works) · Environmental health (1 works) · Human Factors and Ergonomics (1 works) · Human Factors and Ergonomics (1 works)