Abstract
In smart learning ecosystems, online communities act as informal learning environments in which members share their interests, challenges, and knowledge on a specific topic. The life span of such communities depends on sociability and usability, and therefore also on the addition and successful integration of newcomers. This study focuses on the automated classification of blog communities into integrative and non-integrative based on features derived from time series analyses applied to the distribution of contributions in time and of participants’ pauses within the community. The generated features can be used to support learners with insights on which communities should be targeted in order to maximize their chance of integration. Our study is focused on 20 communities which were classified with an 85% accuracy into integrative and non-integrative. For educational research, our findings provide a starting point to follow up investigations on the impact of pauses in learning processes or learners’ behavior.
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Acknowledgements
This work was supported by a grant of the Romanian Ministry of Research and Innovation, CCCDI–UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0689/“Lib2Life-Revitalizarea bibliotecilor si a patrimoniului cultural prin tehnologii avansate”/“Revitalizing Libraries and Cultural Heritage through Advanced Technologies”, within PNCDI III.
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Pasov, I., Dascalu, M., Nistor, N., Trausan-Matu, S. (2020). Prediction of Newcomer Integration in Online Knowledge Building Communities Using Time Series Analyses. In: Rehm, M., Saldien, J., Manca, S. (eds) Project and Design Literacy as Cornerstones of Smart Education. Smart Innovation, Systems and Technologies, vol 158. Springer, Singapore. https://doi.org/10.1007/978-981-13-9652-6_14
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DOI: https://doi.org/10.1007/978-981-13-9652-6_14
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