Academic Performance Based on Gender using Filter Ranker Algorithms – An Experimental Analysis in Sultanate of Oman
Reshmy Krishnan1, Hameetha Beegum2, Sherimon.P.C3
1Dr. Reshmy Krishnan, Computing Department, Muscat College, Sultanate of Oman
2Ms.Hameetha Beegum, Computing Department, Muscat College, Sultanate of Oman
3Dr. Sherimon .P.C, IT Department, Arab Open University Muscat, Sultanate of Oman
Manuscript received on 20 August 2019. | Revised Manuscript received on 09 September 2019. | Manuscript published on 30 September 2019. | PP: 3502-3506 | Volume-8 Issue-11, September 2019. | Retrieval Number: K24900981119/2019©BEIESP | DOI: 10.35940/ijitee.K2490.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Education molds the future society. Student profile analysis in higher education system in sultanate of Oman reveals that drop out cases of students are tremendously increasing for the last few years. Learning environment plays a vital role in providing appropriate teaching methodologies to motivate the skills of the students. Variations in academic performance can be observed based on different educational indicators such as gender, social, economical, cultural and community characteristics. This paper tries to conduct an analysis on the existing data based on educational data mining and tries to make a classification based on gender which helps to adapt necessary teaching methodology to improve the student performance. A data set of 400 students from three colleges in Sultanate of Oman in three consecutive years is taken as case study for this analysis. Since student’s performance is classified as per the data model based on their gender, equal number of male and female data are considered. Data consists of 12 attributes with result as output. Classification of students performance has been achieved using the classification algorithms in WEKA tool. Irrelevant features are eliminated to select subset of input variables in feature selection (FS) algorithm. This is active to improve learning efficiency, predictive accuracy and reduce complexity of learned results. The paper demonstrates gender as the priority attribute to create best data model.
Keywords: Ranking Algorithm, Filters
Scope of the Article: Sequential, Parallel and Distributed Algorithms and Data Structures.