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Academics Mining for Information Analytics as a Method in Improving Student Performance Through Effective Learning Strategies
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Author(s): Froilan De Guzman (University of the East-Caloocan, Philippines), Sheila Abaya (University of the East-Caloocan, Philippines), Vince Benito (University of the East-Caloocan, Philippines)and Irish Mae Chua (University of the East-Caloocan, Philippines)
Copyright: 2017
Pages: 17
Source title:
Handbook of Research on Technology-Centric Strategies for Higher Education Administration
Source Author(s)/Editor(s): Purnendu Tripathi (Indira Gandhi National Open University (IGNOU), India)and Siran Mukerji (Indira Gandhi National Open University (IGNOU), India)
DOI: 10.4018/978-1-5225-2548-6.ch005
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Abstract
Academic analytics in Higher Education Institutions (HEIs) through the promising technology of Data Mining (DM) is considered as the fastest method of generating knowledge discovery in the voluminous student digital data. Applying DM in the area of education will improve the performance of student and with the mined results; it can help educators to devise better teaching strategies for effective student learning. Knowledge discovery in student data can generate possible model for academic planners and educators for the institutional systemic change by improving the teaching, learning, and decision making strategy. Insights and predictive models can also be derived in identifying student performance and success rate. Several DM techniques can be listed and used in higher education such as clustering, classification, visualization, and association analysis. However, the research has emphasis on the technique of clustering using the modified K-Means Algorithm. The silhouette coefficient was incorporated in the K-Means algorithm for automatic direct cluster determination. The result showed that modification of the simple K-Means clustering algorithm through the use of silhouette coefficient achieved the same result in identifying the number of cluster with fewer burdens in the user subjective determination of cluster.
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