A Recommender System for Educational Planning

Mustapha Skittou; Mohamed Merrouchi; Taoufiq Gadi

doi:10.2478/cait-2024-0016

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A Recommender System for Educational Planning

Cybernetics and Information Technologies

Volume 24 (2024): Issue 2 (June 2024)

By: Mustapha Skittou, Mohamed Merrouchi and Taoufiq Gadi

Open Access

|Jun 2024

Abstract

Knowledge-based recommender systems have always had their privileged place among all Decision Support Systems (DSS), given their advantage on several points over other techniques. Our paper presents a framework implementing a hybrid form of Rule-Based Reasoning and Case-Based Reasoning (RBR-CBR), to address the rarely discussed domain of educational planning. The system has been tested and presented outstanding results with a high accuracy, which will benefit educational planners’ decision support. We have also developed a dedicated application for this project to visualize the results obtained.

References

Jain, S., A. Grover, P. S. Thakur, S. K. Choudhary. Trends, Problems and Solutions of Recommender System. – In: Proc. of International Conference on Computing, Communication & Automation, Greater Noida, India, 2015, pp. 955-958. DOI: 10.1109/CCAA.2015.7148534.
Search in Google Scholar Back to article
Singh, N., S. Tripathi, D. P. Singh, P. Bhasker. Recommendation Systems in the Big Data Era. – International Journal of Innovative Technology and Exploring Engineering, Vol. 8, 2019, No 12S3, pp. 80-85.
Search in Google Scholar Back to article
Amutha, S., R. Vikram Surya. An Improved Product Recommender System Using Collaborative Filtering and a Comparative Study of ML Algorithms. – Cybernetics and Information Technologies, Vol. 23, 2023, No 4, pp. 51-62.
Search in Google Scholar Back to article
Mosima, A. M., S. O. Ojo, S. A. Odunaike, H. D. Masethe. Framework of Recommendation Systems for Educational Data Mining (EDM) Methods: CBR-RS with KNN Implementation. – Transactions on Engineering Technologies, Singapore, Springer, 2021, pp. 87-98.
Search in Google Scholar Back to article
Tran, T. D., N. Thanh-Hai, N. Thai-Nghe. Novel Approaches for Searching and Recommending Learning Resources. – Cybernetics and Information Technologies, Vol. 23, 2023, No 2, pp. 151-169.
Search in Google Scholar Back to article
Bansal, S., N. Baliyan. A Study of Recent Recommender System Techniques. – International Journal of Knowledge and Systems Science (IJKSS), Vol. 10, 2019, No 2, pp. 13-41. DOI: 10.4018/IJKSS.2019040102.
Search in Google Scholar Back to article
Alhijawi, B., Y. Kilani. The Recommender System: A Survey. – Int. J. Advanced Intelligence Paradigms, Vol. 15, 2020, No 3, pp. 229-251.
Search in Google Scholar Back to article
Workneh, A., D. Teferi, A. Kumilachew. Knowledge-Based Decision Support System for Detecting and Diagnosis of Acute Abdomen Using Hybrid Approach. – In: F. Mekuria, E. Nigussie, T. Tegegne, Eds. Information and Communication Technology for Development for Africa. ICT4DA 2019. Communications in Computer and Information Science. Vol. 1026. Cham, Springer, 2019. DOI: 10.1007/978-3-030-26630-1_6.
Search in Google Scholar Back to article
Jun, G. Network Data Mining Based on Artificial Intelligence Inference Engine. – Microprocessors and Microsystems, Vol. 82, 2021, 103794. ISSN 0141-9331. DOI: 10.1016/j.micpro.2020.103794.
Search in Google Scholar Back to article
Asif, M., J. Ahmed. A Novel Case Base Reasoning and Frequent Pattern Based Decision Support System for Mitigating Software Risk Factors. – In: IEEE Access, Vol. 8, 2020, pp. 102278-102291. DOI: 10.1109/ACCESS.2020.2999036.
Search in Google Scholar Back to article
Kasie, F. M., G. Bright. Integrating Fuzzy Case-Based Reasoning, Parametric and Feature-Based Cost Estimation Methods for Machining Process. – Journal of Modelling in Management, Vol. 16, 2021, No 3, pp. 825-847. DOI: 10.1108/JM2-05-2020-0123.
Search in Google Scholar Back to article
Smyth, B. Case-Based Recommendation. – In: P. Brusilovsky, A. Kobsa, W. Nejdl, Eds. The Adaptive Web. Lecture Notes in Computer Science. Vol. 4321. Berlin, Heidelberg, Springer, 2007. DOI: 10.1007/978-3-540-72079-9_11.
Search in Google Scholar Back to article
Burke, R. Hybrid Recommender Systems: Survey and Experiments. – User Model User-Adap Inter, Vol. 12, 2002, pp. 331-370. DOI: 10.1023/A:1021240730564.
Search in Google Scholar Back to article
Norzaidah, M. N., A. Ahmad, S. A. Halim, A. M. Ali. Intelligent Tutoring System Using Rule-Based and Case-Based: A Comparison, Procedia. – Social and Behavioral Sciences, Vol. 67, 2012, pp. 454-463. ISSN 1877-0428. DOI: 10.1016/j.sbspro.2012.11.350.
Search in Google Scholar Back to article
Werdiningsih, I., R. Hendradi, P. Purbandini, B. Nuqoba, E. Anna. The Efficient Distance Weighted Case Base Rule (DW-CBR) for Early Childhood Diseases Diagnosis. – International Journal of Computing, Vol. 20, 2021, No 2, pp. 262-269. DOI: 10.47839/ijc.20.2.2174.
Search in Google Scholar Back to article
Ramchand, K., et al. 2018. Towards a Cloud Architectural Decision Framework Using Case-Based Reasoning and Rule-Based Reasoning. – In: Proc. of Australasian Conference on Information Systems, Sydney, 2018. UTS ePRESS. DOI: https://doi.org/10.5130/acis2018.ac.
Search in Google Scholar Back to article
Admass, W. S., Y. Y. Munaye. Integrating Case-Based and Rule-Based Reasoning for Diagnosis and Treatment of Mango Disease Using Data Mining Techniques. – Int. J. Inf. Tecnol., 2023. DOI: 10.1007/s41870-023-01587-y.
Search in Google Scholar Back to article
Renata, S., M. Perkusich, L. Silva, H. Almeida, C. Siebra, A. Perkusich. Early Diagnosis of Gastrointestinal Cancer by Using Case-Based and Rule-Based Reasoning. – In: Expert Systems with Applications, 2016. DOI: 10.1016/j.eswa.2016.05.026.
Search in Google Scholar Back to article
Maoyuan, C., Y. Gao, H. Zhan, J. He, W. Wei. An Inference Model of Driving Environment Relationship Based on Rules and Cases. – In: Proc. of SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, (120791O), 1 December 2021. DOI: 10.1117/12.2622726.
Search in Google Scholar Back to article
Manel, S. Comparing Recommender Systems Using Synthetic Data. – In: Proc. of 12th ACM Conference on Recommender Systems (RecSys’18). Association for Computing Machinery, New York, NY, USA, 2018, pp. 548-552. DOI: 10.1145/3240323.3240325.
Search in Google Scholar Back to article
Lahoud, C., S. Moussa, C. Obeid et al. A Comparative Analysis of Different Recommender Systems for University Major and Career Domain Guidance. – Educ. Inf. Technol., Vol. 28, 2023, pp. 8733-8759. DOI: 10.1007/s10639-022-11541-3.
Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.2478/cait-2024-0016 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702

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Language: English

Page range: 67 - 85

Submitted on: Jan 17, 2024

Accepted on: May 28, 2024

Published on: Jun 27, 2024

Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Recommender system,

Knowledge-based system,

Rule-based reasoning,

Case-based reasoning,

Hybrid system,

Educational planning

Related subjects:

Computer sciences,

Information technology

© 2024 Mustapha Skittou, Mohamed Merrouchi, Taoufiq Gadi, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 24 (2024): Issue 2 (June 2024)