Abstract
The emerging field of educational data mining gives us better perspectives for insights in educational data. This is done by extracting hidden patterns in educational databases. In these lines, the objective of this research work is to introduce hierarchical clustering models for student’s collected data. The ultimate goal is to find attributes in terms of set of clusters which severely affect the student’s performance. Here clustering is intentionally used as the most common causes affecting performance within the database which cannot be seen normally. The results enable us to use discovered characteristic or patterns in palpating student’s learning outcomes. These patterns can be useful for teachers to identify effective prognostication strategies for students.
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Bhalchandra, P. et al. (2016). Prognostication of Student’s Performance: An Hierarchical Clustering Strategy for Educational Dataset. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining—Volume 1. Advances in Intelligent Systems and Computing, vol 410. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2734-2_16
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DOI: https://doi.org/10.1007/978-81-322-2734-2_16
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