Apriori Association Rule for Course Recommender system

  • Fakhri Fauzan Telkom University
  • Dade Nurjanah Telkom University
  • Rita Rismala Telkom University
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Abstract

Until recently, recommender systems have been applied in learning, such as to recommend appropriate courses. They are based on users’ ratings, learning history, or curriculum that provide relationship between courses. The last approach, however, can’t be applied to Massive Open Online Courses (MOOCs) that don’t maintain such information. Hence, course recommender systems for MOOCs must be based on other learners’ experience. This paper discusses such recommender systems. We apply Apriori Association Rule and the case study used in this study is the Canvas Network dataset and the HarvardX-MITx dataset. The proposed recommender system consists of a pre-processing to normalize data and reduce anomalous data, data cleaning to handle empty data, K-Modes clustering to group users, grouping registration transactions for filtering user registration transaction, and finally, rule formation using the Apriori Association Rule. The performance of the association rules obtained, a lift ratio evaluation metric is used. The experiments results show the best parameters in this study are 0.01 for minimum support and 0.6 for minimum confidence. With these two parameters, the number of rules and the average lift ratio value on the Canvas Network dataset are 110 rules and 19.055, while the HarvardX-MITx dataset is 48 rules and 3.662.

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Author Biographies

Fakhri Fauzan, Telkom University
School of Computing
Dade Nurjanah, Telkom University
School of Computing
Rita Rismala, Telkom University
School of Computing

References

N. Bendakir and E. Aimeur, “Using association rules for course recommendation,” Proc. AAAI Work. Educ. Data Min., vol. WS-06-05, pp. 31–40, 2006.

R. Farzan and P. Brusilovsky, “Encouraging user participation in a course recommender system: An impact on user behavior,” Comput. Human Behav., vol. 27, no. 1, pp. 276–284, 2011.

R. Burke, “Aacorn: A CBR recommender for academic advising,” 2005.

S. Ray and A. Sharma, “A collaborative filtering based approach for recommending elective courses,” Commun. Comput. Inf. Sci., vol. 141 CCIS, pp. 330–339, 2011.

F. Ricci, L. Rokach, and B. Shapira, Recommender Systems Handbook. 2011.

C. Network, “Canvas Network Person-Course (1/2014 - 9/2015) De-Identified Open dataset.” Harvard Dataverse, 2016.

Mit. and HarvardX, “HarvardX-MITx Person-Course Academic Year 2013 De-Identified dataset, version 2.0.” Harvard Dataverse, 2014.

S. B. Aher and L. M. R. J. Lobo, “A Comparative Study of Association Rule Algorithms for Course Recommender System in E-learning,” 2012.

N. Manouselis, H. Drachsler, K. Verbert, and E. Duval, Recommender Systems for Learning. Springer-Verlag New York, 2013.

D. P.-N. Tan, D. M. Steinbach, D. A. Karpatne, and D. V. Kumar, Introduction to Data Mining (Second Edition), 2nd Editio. New York, NY: Pearson Education, 2018.

L. M. Sheikh, B. Tanveer, and M. A. Hamdani, “Interesting measures for mining association rules,” in 8th International Multitopic Conference, 2004. Proceedings of INMIC 2004., 2004, pp. 641–644.

R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” 20th Int. Conf. Very Large Data Bases, pp. 487–499, 1994.

Suyanto, Data Mining untuk Klasifikasi dan Klasterisasi Data. Bandung, Indonesia: Informatika Bandung, 2017.

S. B. Aher and L. M. R. J. Lobo, “Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data,” Knowledge-Based Syst., vol. 51, pp. 1–14, 2013.

Suyanto, Machine Learning: Tingkat Dasar dan Lanjut. Bandung, Indonesia: Informatika Bandung, 2018.

M. Awad and R. Khanna, Machine Learning in Action. 2015.

M. Hahsler, B. Grün, and K. Hornik, “Introduction to arules - Mining Association Rules and Frequent Item Sets,” October, pp. 1–28, 2006.

W.-Y. Lin, M.-C. Tseng, and J.-H. Su, “A Confidence-Lift Support Specification for Interesting Associations Mining,” pp. 148–158, 2007.

J. P. Jiawei Han, Micheline Kamber, Data Mining – Concepts & Techniques. 2011.

Published
2020-10-02
How to Cite
Fauzan, F., Nurjanah, D., & Rismala, R. (2020). Apriori Association Rule for Course Recommender system. Indonesian Journal on Computing (Indo-JC), 5(2), 1-16. https://doi.org/10.34818/INDOJC.2020.5.2.434
Section
Computer Science