Collaborative Learning in Data Science Education: A Data Expedition as a Formative Assessment Tool. Maksimenkova, O.; Neznanov, A.; and Radchenko, I. In Advances in Intelligent Systems and Computing, volume 916, pages 14-25, 2020. Springer Verlag.
abstract   bibtex   
The paper addresses the questions of data science education of current importance. It aims to introduce and justify the framework that allows flexibly evaluate the processes of a data expedition and a digital media created during it. For these purposes, the authors explore features of digital media artefacts which are specific to data expeditions and are essential to accurate evaluation. The rubrics as a power but hardly formalizable evaluation method in application to digital media artefacts are also discussed. Moreover, the paper documents the experience of rubrics creation according to the suggested framework. The rubrics were successfully adopted to two data-driven journalism courses. The authors also formulate recommendations on data expedition evaluation which should take into consideration structural features of a data expedition, distinctive features of digital media, etc.
@inProceedings{
 title = {Collaborative Learning in Data Science Education: A Data Expedition as a Formative Assessment Tool},
 type = {inProceedings},
 year = {2020},
 identifiers = {[object Object]},
 keywords = {Collaborative technologies,Data expedition,Data science,Education},
 pages = {14-25},
 volume = {916},
 publisher = {Springer Verlag},
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 abstract = {The paper addresses the questions of data science education of current importance. It aims to introduce and justify the framework that allows flexibly evaluate the processes of a data expedition and a digital media created during it. For these purposes, the authors explore features of digital media artefacts which are specific to data expeditions and are essential to accurate evaluation. The rubrics as a power but hardly formalizable evaluation method in application to digital media artefacts are also discussed. Moreover, the paper documents the experience of rubrics creation according to the suggested framework. The rubrics were successfully adopted to two data-driven journalism courses. The authors also formulate recommendations on data expedition evaluation which should take into consideration structural features of a data expedition, distinctive features of digital media, etc.},
 bibtype = {inProceedings},
 author = {Maksimenkova, Olga and Neznanov, Alexey and Radchenko, Irina},
 booktitle = {Advances in Intelligent Systems and Computing}
}
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