Using Collaboration Engineering to Mitigate Low Participation, Distraction, and Learning Inefficiency to Support Collabo
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Using Collaboration Engineering to Mitigate Low Participation, Distraction, and Learning Inefficiency to Support Collaborative Learning in Industry Xusen Cheng1 · Shixuan Fu2,3 · Gert‑Jan de Vreede4 · Yuanyuan Li5 Accepted: 16 September 2020 © Springer Nature B.V. 2020
Abstract Computer-supported collaborative learning (CSCL) is widely adopted in industry learning, but it still faces challenges, including low participation, distraction, and learning inefficiency. In our study, we follow the design science research method to develop artifacts (a process and discussion platform) to address these CSCL challenges. Collaboration engineering was used as our design theory. A Discussion Platform was designed as a tool to help non-expert practitioner instruct collaborative learning process. We carried out evaluations on the two designed artifacts through 81 managers working in various industries through a mixed-method approach, including survey and qualitative interviews. We find that our designed artifacts receive high satisfaction in industry CSCL and reduce problems of low participation, distraction, and learning inefficiency. We identified several factors that contribute to the problem solving of low participation, distraction and inefficiency in industry CSCL, including usability, expression affordance, process guidance, goal clarity, flexibility affordance, thinkLet instruction, and flow experiences. Keywords Participation · Attention · Efficiency · Collaboration support system · Computer-supported collaborative learning · Collaboration engineering
* Shixuan Fu [email protected] 1
School of Information, Renmin University of China, Beijing 100872, China
2
School of Tourism Science, Beijing International Studies University, No. 1 Dingfuzhuangnanli, Chaoyang District, Beijing 100024, China
3
Research Center of Beijing Tourism Development, Beijing 100024, China
4
Muma College of Business, University of South Florida, Tampa 33620, USA
5
School of Information Technology and Management, University of International Business and Economics, Beijing 100029, China
13
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X. Cheng et al.
1 Introduction In today’s ever-increasing competition environment, successful industry learning helps facilitate organizations’ competitive advantage by generating new knowledge and skills (Balasubramanian and Lieberman 2010), leading to the improvement of industry decision making (Bryson and Mobolurin 1997) and organizational performance (Santos-Vijande et al. 2012). Meanwhile, since staff training is important in workplaces, managers have to consider improving internal exploitative learning and facilitating knowledge transfer among employees (Lichtenthaler 2013). Thus, industry learning is helpful for organizations to develop continuously and manage human resources. As with all learning types, industry learning went through several stages. For decades, traditional lecturing used an instructor-oriented approach, in which the majority of the learning time depended on the instructor’s delivery of knowledge. The d
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