Abstract—The analysis and assessment of the students’
feedback in improving the educational environment as well as
enhancing students' learning experience is one of the critical
issues for the higher education community. The conventional
methods of analysis and assessment are not sufficient to explore
the hidden information from the student feedback data
repositories. In this paper we present the analysis of students’
feedback data using k-means clustering algorithm for effective
decision making by educational community responsible for
monitoring and reviewing the effectiveness of educational
programs and for improving the quality of teaching and
learning experience for their students.
Index Terms—Centroid, data mining, homogeneous groups,
k-means.
Anwar Muhammad Abaidullah and Edriss Ali are with the College of
Engineering and Computing of Al Ghurair University, 37374 UAE (e-mail:
{anwar, edirss}@agu.ac.ae).
Naseer Ahmed is with Al Ghurair University, 37374 UAE (e-mail:
naseer@agu.ac.ae).
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Cite:Anwar Muhammad Abaidullah, Naseer Ahmed, and Edriss Ali, "Identifying Hidden Patterns in Students‟ Feedback through Cluster Analysis," International Journal of Computer Theory and Engineering vol. 7, no. 1, pp. 16-20, 2015.
