Predicting Student Success in Courses via Collaborative Filtering

Article English OPEN
Cakmak, Ali (2017)
  • Publisher: Advanced Technology and Science (ATScience)
  • Journal: International Journal of Intelligent Systems and Applications in Engineering (issn: 2147-6799)
  • Related identifiers: doi: 10.18201/ijisae.2017526690
  • Subject: Collaborative Filtering; Educational Data Mining; Student Success Estimation; Outlier Elimination
    acm: 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%. 
  • References (20)
    20 references, page 1 of 2

    Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook (pp. 1-35). Springer US.

    Benesty, J., Chen, J., Huang, Y., & Cohen, I. (2009). Pearson correlation coefficient. In Noise reduction in speech processing (pp. 1-4). Springer Berlin Heidelberg.

    Gower, J. C. (1985). Properties of Euclidean and nonEuclidean distance matrices. Linear Algebra and its Applications, 67, 81-97.

    Cha, S. H. (2007). Comprehensive survey on distance/similarity measures between probability density functions. City, 1(2), 1.

    Sukhija, K., Jindal, M., & Aggarwal, N. (2015). The recent state of educational data mining: A survey and future visions. In MOOCs, Innovation and Technology in Education (MITE), 2015 IEEE 3rd International Conference on (pp. 354-359). IEEE.

    Verma, K., Singh, A., & Verma, P. (2016). A Review on Predicting Student Performance Using Data Mining Method. Futuristic Trends in Engineering, Science, Humanities, and Technology (FTESHT-16), pp. 124 - 129.

    Sullare, V. A., Thakur, R. S., & Mishra, B. (2016). Analysis of Student Performance Using Mining Technique: A Review. Artificial Intelligent Systems and Machine Learning, 8(3), 94-97.

    Luo, J., Sorour, S. E., Goda, K., & Mine, T. (2015).

    Predicting Student Grade Based on Free-Style Comments Using Word2Vec and ANN by Considering Prediction Results Obtained in Consecutive Lessons. International Educational Data Mining Society.

    [9] Luo, J., Sorour, S. E., Goda, K., & Mine, T. (2015).

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