Abstract
Reducing students’ high dropout rates in the computer programming courses is a challenging problem of great concern in computer science education. Online Judge (OJ) systems were recently being investigated to address this problem and promote computer programming education. Most of the existing OJ systems have been confined only for evaluation purposes, and do not provide any personalized recommendations to enhance the productivity of a student. With this motivation, this paper proposes a novel rule-based OJ recommender system to promote computer programming education. The proposed system involves the following five steps: (i) scoring the programs submitted by a student automatically, (ii) generation of a transactional database, (iii) clustering the database with respect to their scores and other evaluation parameters, (iv) discovering interesting association rules that exist in each of the cluster’s data, and (v) providing appropriate recommendations to the users. Experimental results on the data generated by a real-world OJ system demonstrate that the proposed system is efficient.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Margulieux, L.E., Morrison, B.B., Decker, A.: Reducing withdrawal and failure rates in introductory programming with subgoal labeled worked examples. Int. J. STEM Educ. 7(1), 1–16 (2020). https://doi.org/10.1186/s40594-020-00222-7
Wasik, S., Antczak, M., Badura, J., Laskowski, A., Sternal, T.: A survey on online judge systems and their applications. ACM Comput. Surv. (CSUR) 51(1), 1–34 (2018)
Revilla, M.A., Manzoor, S., Liu, R.: Competitive learning in informatics: the UVa online judge experience. Olympiads Inf. 2, 131–148 (2008)
Petit, J., et al.: Jutge.org: characteristics and experiences. IEEE Trans. Learn. Technol. 11(3), 321–333 (2018)
Bez, J.L., Tonin, N.A., Rodegheri, P.R.: URI online judge academic: a tool for algorithms and programming classes. In: Proceedings of the 2014 9th International Conference on Computer Science Education, Vancouver, BC, Canada, pp. 149–152 (2014)
Watanobe, Y.: Aizu online judge (2018). https://onlinejudge.u-aizu.ac.jp
Saito, T., Watanobe, Y.: Learning path recommendation system for programming education based on neural networks. Int. J. Distance Educ. Technol. (IJDET) 18(1), 36–64 (2020)
Klašnja-Milićević, A., Ivanović, M., Nanopoulos, A.: Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artif. Intell. Rev. 44, 571–604 (2015)
Khanal, S.S., Prasad, P.W.C., Alsadoon, A., Maag, A.: A systematic review: machine learning based recommendation systems for e-learning. Educ. Inf. Technol. 25(4), 2635–2664 (2019). https://doi.org/10.1007/s10639-019-10063-9
Yusof, N., Zin, N.A.M., Adnan, N.S.: Java programming assessment tool for assignment module in moodle e-learning system. In: Proceedings of the International Conference on Teaching and Learning in Higher Education in conjunction with Regional Conference on Engineering Education and Research in Higher Education, Malaysia, pp. 767–773 (2012)
Okubo, F., Yamashita, T., Shimada, A., Konomi, S.: Students’ performance prediction using data of multiple courses by recurrent neural network. In: Proceedings of the 25th International Conference on Computers in Education (ICCE), Christchurch, New Zealand, pp. 439–444 (2017)
Mekterović, I., Brkić, L., Milašinović, B., Baranović, M.: Building a comprehensive automated programming assessment system. IEEE Access 8, 81154–81172 (2020)
Rahman, M.M., Watanobe, Y.: An efficient approach for selecting initial centroid and outlier detection of data clustering. In: Proceedings of the 18th International Conference on Intelligent Software Methodologies, Tools and Techniques (SoMeT19), Kuching, Malaysia, pp. 616–628 (2019)
Marutho, D., Hanadaka, S.H., Wijaya, E., Muljono: The determination of cluster number at k-mean using elbow method and purity evaluation on headline news. In: Proceedings of the 2018 International Seminar on Application for Technology of Information and Communication, Semarang, Indonesia, pp. 533–538 (2018)
Yuan, C., Yang, H.: Research on k-value selection method of k-means clustering algorithm. J-Multidiscip. Sci. J. 2(2), 226–235 (2019)
Kamsu-Foguem, B., Rigal, F., Mauget, F.: Mining association rules for the quality improvement of the production process. Expert Syst. Appl. 40(4), 1034–1045 (2013)
Yuan, J., Ding, S.: Research and improvement on association rule algorithm based on FP-growth. In: Proceedings of the International Conference on Web Information Systems and Mining, China, pp. 306–313 (2012)
Wang, P., An, C., Wang, L.: An improved algorithm for mining association rule in relational database. In: Proceedings of the International Conference on Machine Learning and Cybernetics, Lanzhou, China, pp. 247–252 (2014)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, New York, USA, pp. 1–12 (2000)
Rahman, M.M., Watanobe, Y., Nakamura, K.: Source code assessment and classification based on estimated error probability using attentive LSTM language model and its application in programming education. Appl. Sci. 10(8), 2973 (2020)
Rahman, M.M., Watanobe, Y., Nakamura, K.: A neural network based intelligent support model for program code completion. Sci. Program. 2020, 1–18 (2020)
Kiran, R.U.: FP-growth library (2020). https://github.com/udayRage/pami_pykit/ tree/master/traditional/fpgrowth
Acknowledgement
This research was funded by the Japan Society for the Promotion of Science (JSPS) KAKENHI (Grant Number 19K12252).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interests.
Ethics Approval
This research was approved by the Research Ethics Examination Boards, The University of Aizu, Japan.
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Rahman, M.M., Watanobe, Y., Rage, U.K., Nakamura, K. (2021). A Novel Rule-Based Online Judge Recommender System to Promote Computer Programming Education. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12799. Springer, Cham. https://doi.org/10.1007/978-3-030-79463-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-79463-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-79462-0
Online ISBN: 978-3-030-79463-7
eBook Packages: Computer ScienceComputer Science (R0)