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Model and Practice of Crowd-Based Education

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Web and Big Data (APWeb-WAIM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11268))

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Abstract

Based on connectivism pedagogy crowd-based education provides a practical method to extensively exploit wisdoms of core learners in education organization and external crowds on Internet. However, when applying such a method in education field, several design questions about why, what, when, who, where and how to adopt such method should be clarified and elaborated. In this paper, we introduce our successful applications of the crowd-based method in software engineering course for undergraduates. We design an organization structure consisting of “small-core crowd” and “large-external crowd” for our course projects in which both learners and crowds on Internet work together to contribute their wisdoms. Two kinds of wisdoms of crowds are exploited in our practices. One is the high-quality open source software (OSS) developed by crowds on the Internet, the other is the diverse software development issues, knowledges, experiences, expertise, etc., that are discussed and interacted by crowds in OSS communities and course communities. We design several course practice activities to exploit crowds’ wisdoms, including reading high-quality OSS, searching and reusing OSS in course project, joining and getting helps from OSS communities. The results show that the crowd-based method applied in our software engineering course can significantly improve learner’s engineering capabilities of developing high-quality and large-scale software.

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Acknowledgments

We greatly thank the project supports of National Key R&D Program of China (2018YFB1004202) and National Science Foundation of China (61532004), the undergraduates that participate in the practices, the Trustie development and technical support team.

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Correspondence to Xinjun Mao .

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Mao, X., Lu, Y., Yin, L., Wang, T., Yin, G. (2018). Model and Practice of Crowd-Based Education. In: U, L., Xie, H. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 11268. Springer, Cham. https://doi.org/10.1007/978-3-030-01298-4_25

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  • DOI: https://doi.org/10.1007/978-3-030-01298-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01297-7

  • Online ISBN: 978-3-030-01298-4

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