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Cakmak, Ali (2017)
Publisher: Advanced Technology and Science (ATScience)
Journal: International Journal of Intelligent Systems and Applications in Engineering
Languages: English
Types: Article
Subjects: Collaborative Filtering; Educational Data Mining; Student Success Estimation; Outlier Elimination

Classified by OpenAIRE into

ACM Ref: ComputingMilieux_COMPUTERSANDEDUCATION
Based on their skills and interests, students’ success in courses may differ greatly. Predicting student success in courses before they take them may be important. For instance, students may choose elective courses that they are likely to pass with good grades. Besides, instructors may have an idea about the expected success of students in a class, and may restructure the course organization accordingly. In this paper, we propose a collaborative filtering-based method to estimate the future course grades of students. Besides, we further enhance the standard collaborative filtering by incorporating automated outlier elimination and GPA-based similarity filtering. We evaluate the proposed technique on a real dataset of course grades. The results indicate that we can estimate the student course grades with an average error rate of 0.26, and the proposed enhancements improve the error value by 16%. 
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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