Use of collaborative concept mapping in team diagnosis. Issue 5 (13th March 2021)

• Record Type: Journal Article
• Authors: Pan, Dan; Wang, Tingyi; Zhang, Xuegang; Jia, Ming; Li, Zhizhong
• Is Part Of: Human factors and ergonomics in manufacturing and service industries. Volume 31:Issue 5(2021)
• Page Start: 469
• Page End: 483
• Publication Date: 2021-03-13
• Subjects: collaborative concept mapping -- nuclear power plant -- task complexity -- team diagnosis -- workload; Computer integrated manufacturing systems -- Periodicals; Human engineering -- Periodicals; 620.8205
• DOI: 10.1002/hfm.20894
• Languages: English
• ISSNs: 1090-8471

Abstract

Abstract: Teamwork is necessary to accomplish challenging and information‐rich tasks in complex industrial systems, such as fault diagnosis in nuclear power plants. To improve team performance, assistive visualization tools are used to promote communication and reduce cognitive conflicts. As a shared visualization technology, collaborative concept mapping can effectively externalize one's thoughts into a visualized structure, thereby improving the efficiency of communication and information processing. This study accordingly investigates the effects of collaborative concept mapping on team diagnosis in two fault situations with different complexity levels in a simulated nuclear power plant: one caused by a single initiating event, and the other triggered by two initiating events. The dependent variables included fault analysis score, diagnostic result score, diagnosis time, workload, score of concept map content, and fixation time on a teammate's concept map. The findings revealed that collaborative concept mapping significantly improved the diagnostic result score and shortened diagnosis time, and marginally but significantly increased the fault analysis score. However, under the more complex fault situation triggered by two initiating events, this tool also significantly increased participants' physical load and frustration level. Future work should focus on how collaborative concept mapping can improve operator performance while causing as little extra workload as possible, as well as explore its effects on experienced operators.