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Enhanced Elective Subject Selection for ICSE School Students using Machine Learning Algorithms
Objective: Academic advising requires a lot of expertise, time and responsibility. To assist the human advisors in an efficient way, upcoming of computerized advising system is a necessity Methods/Statistical Analysis: Course Advisory System has been implemented using WEKA tool to recommend subjects for 8th class students of ICSE board. Machine learning algorithms – Naïve Bayes, J48, PART, Random Forest and KNN have been modeled and tested on the data set. The performance of each classifier has been compared and analyzed Findings: It is inferred that no advising system has been developed to assist school students in subject selection. Research work based on Indian students’ requirements is minimal. Research work based on students’ data caters more on binary class problems whereas the addressing of multi class problems is minimal. This work proposes an advising system for the school students of 8th standard of ICSE board to choose their electives. Application/Improvements: This work focuses on Indian educational system of school students. The approach takes care of the school students which will add its advantage to the existing systems. As school students are more vulnerable by taking wrong decisions, the course Advisory system will assist them in analyzing their academic history and help them choose their electives wisely. The classification algorithms might give a better accuracy with increasing instances. The Course advisory system can be enhanced using ensemble approach.
Course Advisory System, Feature Selection Algorithms, ICSE School Students, Machine Learning Algorithms, Subject Selection
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