Title: Revealing deep interaction patterns of team learning processes through video-based interactive analysis

Authors: Lei Xie; Michael Beyerlein; Soo Jeoung Han

Addresses: Texas State University, 601 University Drive, San Marcos, TX 78666, USA ' Texas A&M University, 555 Harrington Office Building, TAMU, College Station, TX 77840, USA ' Yonsei University, 50 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul, South Korea

Abstract: New tools provide new lenses for a better understanding of how learning occurs within the team setting. A better understanding of team learning will enable human resource development (HRD) professionals to more effectively develop an enhanced level of performance in the workplace. Since teams represent complex learning systems, traditional questionnaires that take a static 'snapshot' of the reality and interviews that capture retrospective member memories fail to reflect the dynamics or the holistic nature of teamwork. Deeper patterns of the dynamics of team learning will provide the foundation for more generalisable theories. We argue that a better understanding of team learning in HRD will grow when scholars generate new models of team learning systems based on new ways of measuring team behaviour that capture the complex interactions among members. In this paper, we propose that video-based interaction analysis can offer more opportunities. We discuss the current research methods issues, profile the current state of the scholarship of team learning, and recommended a new data collection/analysis approach: video-based interactive analysis (VIA).

Keywords: video-based interactive analysis; VIA; team learning; human resource development; HRD.

DOI: 10.1504/IJHRDM.2021.120307

International Journal of Human Resources Development and Management, 2021 Vol.21 No.4, pp.267 - 287

Received: 27 Nov 2019
Accepted: 30 Oct 2020
Published online: 14 Jan 2022 *

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