Abstract
The educational data mining plays an important role in understanding the behavior of students as well as analyzing, evaluating, and predicting the performance of the students at all level. The group work is also an inevitable part of the education domain. The tremendous rise in the use of online tools by students have created the opportunity to generate high-level views of information about the behavior of the students working on any particular project. This collaborative data can be useful for assessment of the project work on individual as well as group basis by using the data mining techniques. In this paper, we aim to present a framework which exploits this data to transform it into understandable format and use it for students’ performance analysis and assessment. The result of k-means algorithm of clustering identifies the different levels of project groups as well as individual group members.
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Shelke, N.P., Gupta, P. (2019). A Computational Approach for Project Work Performance Analysis Based on Online Collaborative Data. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_5
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DOI: https://doi.org/10.1007/978-981-13-1498-8_5
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