Abstract
A growing number of successful mass collaboration projects in various fields show profound changes in the way communities operate and act collectively. One emerging application of mass collaboration is for collective learning, in which a mass of minds jointly drives the effort of building and acquiring knowledge. Such attempt builds on a reservoir of raw knowledge that develops as each contributor shares his/her own partial experience and knowledge. A key element in this process is to ensure that such created knowledge is reliable and trustworthy. This leads to the need of effective assessment mechanisms. Furthermore, the process of learning through mass collaboration needs to be better understood. For this purpose, this work includes a summary of a systematic review of recent literature with the aim of identifying affecting factors and constituents of mass collaborative learning namely, the type of organizational structures, collaborative learning approaches, adopted technologies, and adopted methods for evaluating the quality of performance and knowledge. Based on the findings, a research strategy focused on the quality of collective learning is then proposed.
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This work was funded in part by the Center of Technology and Systems of Uninova and the Portuguese FCT-PEST program UID/EEA/00066/2013 (Impactor project).
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Zamiri, M., Camarinha-Matos, L.M. (2018). Learning Through Mass Collaboration - Issues and Challenges. In: Camarinha-Matos, L., Adu-Kankam, K., Julashokri, M. (eds) Technological Innovation for Resilient Systems. DoCEIS 2018. IFIP Advances in Information and Communication Technology, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-319-78574-5_1
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