Abstract
An ideal scenario for educational research is to perform an experiment, report and publish results, make the results and data available for verification, and finally allow the data to be used in follow up experiments or for secondary analyses. Unfortunately, this scenario often fails after the results are published. Researchers move on to new data and the old data may linger on a legacy server for a short while before disappearing or becoming impossible to comprehend. Managing the dataset lifecycle is a way to address this problem. DataShop (http://pslcdatashop.org) is a central hub for data management of educational data, and in this paper we show how DataShop fits into the dataset lifecycle.
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© 2011 Springer-Verlag Berlin Heidelberg
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Stamper, J.C. et al. (2011). Managing the Educational Dataset Lifecycle with DataShop. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science(), vol 6738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21869-9_100
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DOI: https://doi.org/10.1007/978-3-642-21869-9_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21868-2
Online ISBN: 978-3-642-21869-9
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