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Use of Subspace Clustering Algorithm for Students' Competency and Subject Knowledge Assessment

Use of Subspace Clustering Algorithm for Students' Competency and Subject Knowledge Assessment

Dimple V. Paul, Chitra S. Nayagam
Copyright: © 2018 |Volume: 9 |Issue: 2 |Pages: 14
ISSN: 1947-8208|EISSN: 1947-8216|EISBN13: 9781522545439|DOI: 10.4018/IJKSS.2018040104
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MLA

Paul, Dimple V., and Chitra S. Nayagam. "Use of Subspace Clustering Algorithm for Students' Competency and Subject Knowledge Assessment." IJKSS vol.9, no.2 2018: pp.70-83. http://doi.org/10.4018/IJKSS.2018040104

APA

Paul, D. V. & Nayagam, C. S. (2018). Use of Subspace Clustering Algorithm for Students' Competency and Subject Knowledge Assessment. International Journal of Knowledge and Systems Science (IJKSS), 9(2), 70-83. http://doi.org/10.4018/IJKSS.2018040104

Chicago

Paul, Dimple V., and Chitra S. Nayagam. "Use of Subspace Clustering Algorithm for Students' Competency and Subject Knowledge Assessment," International Journal of Knowledge and Systems Science (IJKSS) 9, no.2: 70-83. http://doi.org/10.4018/IJKSS.2018040104

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Abstract

Student performance studies are the primary challenge for any course with continuous assessment. The challenge lies in performing validation tests of whether course objectives are being met and also in identifying areas of the course structure that needs improvement. This article identifies whether objectives of the course are being achieved or not, by analyzing the student performance in different courses using competencies as the criteria for assessment. Performance evaluation includes diverse types of competency components such as presentation, assignment, group discussion, etc., along with written examination in order to assess the knowledge of students, as well as their interest in the subject. A PROCLUS algorithm has been chosen for experimentation, as the algorithm identifies similarities among data sets and forms clusters of disjoint sets. The algorithm not only considers random sample points, but also successfully scans entire data sets to identify meaningful dimensions that are needed to form actual clusters. Experimental results have identified the similarities of the students' performance across the subjects that are similar in nature and their competency parameters were also found to be similar. A majority of the students have performed alike in certain subjects that involved practical components or in other ways, similar performance is achieved during the assessment of courses on competencies like presentations skills, group discussions, writing skills, etc., rather than mere theoretical components. This study could help to modify the evaluation and assessment pattern for the theory subjects and/or to fine tune the course structure and objectives of such course, and also to find some alternate techniques to improve the other competencies.

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