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https://evnuir.vnu.edu.ua/handle/123456789/23654Повний запис метаданих
| Поле DC | Значення | Мова |
|---|---|---|
| dc.contributor.author | Yunchyk, Valentina | - |
| dc.contributor.author | Fedoniuk, Yurii | - |
| dc.date.accessioned | 2024-02-15T08:51:19Z | - |
| dc.date.available | 2024-02-15T08:51:19Z | - |
| dc.date.issued | 2023-06-30 | - |
| dc.identifier.citation | Yunchyk V. Fedoniuk Y. Results of developing the recommendation system for electronic educational resource selection. Manažérska informatika: vedecký časopis o informatike, Univerzita Komenského v Bratislave, Slovakia. vol.1. 2023, no 1. ISSN: 2728-8310. URL: https://manazerskainformatika.sk/results-of-developing-the-recommendation-system-for-electronic-educational-resource-selection/. | uk_UK |
| dc.identifier.uri | https://evnuir.vnu.edu.ua/handle/123456789/23654 | - |
| dc.description.abstract | This paper proposes developing a recommendation system based on fuzzy logic methods for expert evaluation of electronic educational resources (EERs) and decision-making regarding selecting the most effective resources for educational processes. The concepts of recommendation systems for selecting optimal EERs are examined and analyzed. Scientific publications on expert evaluation and recommendation system utilization are reviewed. The overall structure of the recommendation system is presented, along with descriptions of its subsystems. Fuzzy logic methodologies are used for the assessment of EERs, with a welldefined procedural framework and explicit algorithmic representation. Expert analysis results in a compilation of recommended EER alternatives that align with specified criteria. The EER selection recommendation system is further elucidated through the generation of UML diagrams delineating use cases, sequences, and activities. The initial phases of user engagement with the recommendation system are described in depth, facilitating the selection of electronic learning resources. The recommendation system is introduced with a three-tier architecture consisting of presentation, application, and data administration layers. EER collection, expert criterion-based evaluation input, recommendation rating computation, recommended resource list formation, data visualization, authorization implementation, access provisioning, and creation of the administrator interface are the main technological elements that are described in detail. For each of these elements, implementation strategies and tools are explained. Programming code for the development of each of the key recommendation system stages is provided. The most significant elements of the web application interface are demonstrated, including the recommendation system's criterion selection page, administrator panel, category modification and addition panel, and the recommendation system's output. | uk_UK |
| dc.language.iso | en | uk_UK |
| dc.relation.uri | https://manazerskainformatika.sk/results-of-developing-the-recommendation-system-for-electronic-educational-resource-selection/ | uk_UK |
| dc.subject | recommendation system | uk_UK |
| dc.subject | electronic educational resources | uk_UK |
| dc.subject | fuzzy logic | uk_UK |
| dc.subject | expert evaluation | uk_UK |
| dc.subject | UML diagrams | uk_UK |
| dc.subject | recommendation system architecture | uk_UK |
| dc.title | Results of developing the recommendation system for electronic educational resource selection | uk_UK |
| dc.type | Article | uk_UK |
| dc.citation.journalTitle | Manažérska informatika: vedecký časopis o informatike | - |
| dc.contributor.affiliation | Lesya Ukrainka Volyn National University | uk_UK |
| dc.coverage.country | SK | uk_UK |
| dc.format.pages | 28 | - |
| dc.relation.referencesen | Pasichnyk, V. et al. Using fuzzy logic in the process of expert evaluation of learning resources. Scientific Bulletin of UNFU, vol. 32(4), 2022, pp. 66-76. https://doi.org/10.36930/40320411 | uk_UK |
| dc.relation.referencesen | Esteban, A. et al. Helping university students choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization. Knowl.-Based Syst., vol. 194, p. 105385, Apr. 2020, doi: 10.1016/j.knosys.2019.105385 | uk_UK |
| dc.relation.referencesen | Cañas, A. et al. A Recommender System for Non-traditional Educational Resources: A Semantic Approach. J. Univers. Comput. Sci., vol. 21, no. 2, pp. 306–325, 2015 | uk_UK |
| dc.relation.referencesen | Ko, H. et al. A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields. Electronics, vol. 11, no. 1, p. 141, Jan. 2022, doi: 10.3390/electronics11010141 | uk_UK |
| dc.relation.referencesen | Ojokoh, B. et al. A fuzzy logic-based personalized recommender system. Int. J. Comput. Sci. Inf. Technol. Secure., vol. 2, no. 5, pp. 1008–1015, 2012 | uk_UK |
| dc.relation.referencesen | Artemenko, O. et al. E-tourism recommender systems: a survey and development perspectives. Econtechmod, vol. 6, no. 2, pp. 91–95, 2017 | uk_UK |
| dc.relation.referencesen | Wang, F. Personalized Recommendation System of College Students’ Employment Education Resources Based on Cloud Platform. in e-Learning, eEducation, and Online Training, W. Fu and G. Sun, Eds., in Lecture Notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering, vol. 454. Cham: Springer Nature Switzerland, 2022, pp. 318–333. doi: 10.1007/978-3-031-21164-5_25 | uk_UK |
| dc.relation.referencesen | Lin, J. et al. Intelligent Recommendation System for Course Selection in Smart Education. Procedia Comput. Sci., vol. 129, pp. 449–453, 2018, doi: 10.1016/j.procs.2018.03.023 | uk_UK |
| dc.relation.referencesen | Shu, J. et al. A content-based recommendation algorithm for learning resources. Multimedia. Syst., vol. 24, no. 2, pp. 163–173, Mar. 2018, doi: 10.1007/s00530-017-0539-8 | uk_UK |
| dc.relation.referencesen | Xu Z. et al. Study on Personalized Recommendation Algorithm of Online Educational Resources Based on Knowledge Association. Comput. Intell. Neurosci., vol. 2022, pp. 1–9, Sep. 2022, doi: 10.1155/2022/2192459 | uk_UK |
| dc.relation.referencesen | Slimani, H. et al. Semantic recommendation system of digital educational resources. in Proceedings of the 12th International Conference on Intelligent Systems: Theories and Applications, Rabat Morocco: ACM, Oct. 2018, pp. 1–6. doi: 10.1145/3289402.3289513. | uk_UK |
| dc.relation.referencesen | Kotsyuba, I. et al. Recommendation web service for choosing an individual educational path in the field of transportation systems. programming Transp. Res. Procedia, vol. 63, pp. 591–599, 2022, doi: 10.1016/j.trpro.2022.06.052 | uk_UK |
| dc.relation.referencesen | Morales, A. et al. Recommendation system with graph-oriented databases for a repository of open educational resources. IOP Conf. Ser. Mater. Sci. Eng., vol. 1154, no. 1, p. 012021, Jun. 2021, doi: 10.1088/1757-899X/1154/1/012021 | uk_UK |
| dc.relation.referencesen | Dwivedi, P. et al. e-Learning recommender system for a group of learners based on the unified learner profile approach. Expert Syst., vol. 32, no. 2, pp. 264–276, Apr. 2015, doi: 10.1111/exsy.12061 | uk_UK |
| dc.relation.referencesen | Tarus, J. K. et al. Knowledge-based recommendation: a review of ontologybased recommender systems for e-learning. Artif. Intell. Rev., vol. 50, no. 1, pp. 21–48, Jun. 2018, doi: 10.1007/s10462-017-9539-5 | uk_UK |
| dc.relation.referencesen | Pasichnyk, V. et al. Selection of electronic educational resources using a recommendation system. in Theses of the reports of the 12th International Scientific and Practical Conference 'Mathematics. Information Technologies. Education in Information Technologies, vol. XII. Lutsk-Svitiaz: Lesya Ukrainka Volyn National University, Jun. 2023, pp. 129–131 | uk_UK |
| dc.relation.referencesen | Pasichnyk, V. et al. Model of the Recommender System for the Selection of Electronic Learning Resources. CEUR Workshop Proc. 5 Rd Int. Workshop Mod. Mach. Learn. Technol. Data Sci., no. 3426, pp. 344–355, 2023 | uk_UK |
| dc.relation.referencesen | Yunchyk, V. et al. Application of the hierarchy analysis method for the choice of the computer mathematics system for the IT-sphere specialist's preparation. J. Phys. Conf. Ser., vol. 1840, no. 1, p. 012065, Mar. 2021, doi: 10.1088/1742-6596/1840/1/012065 | uk_UK |
| dc.relation.referencesen | Pasichnyk, V. et al. Comparative characteristics of the functionality of the system of computer mathematics in the process of task solving. Journal of Lviv Polytechnic National University "Information Systems and Networks", no. 11, pp. 87–102, Jun. 2022, doi: 10.23939/sisn2022.11.087 | uk_UK |
| dc.relation.referencesen | Liu, Y. Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion, Int. J. Emerg. Technol. Learn., vol. 18, no. 07, pp. 131–146, Apr. 2023, doi: 10.3991/ijet.v18i07.39233 | uk_UK |
| dc.relation.referencesen | Thongchotchat, V. et al. Educational Recommendation System Utilizing Learning Styles: A Systematic Literature Review. IEEE Access, vol. 11, pp. 8988– 8999, 2023, doi: 10.1109/ACCESS.2023.3238417 | uk_UK |
| dc.relation.referencesen | Felfernig, A. et al. Constraint-Based Recommender Systems. in Recommender Systems Handbook, F. Ricci, L. Rokach, and B. Shapira, Eds., Boston, MA: Springer US, 2015, pp. 161–190. doi: 10.1007/978-1-4899-7637-6_5 | uk_UK |
| dc.relation.referencesen | Jain, R. A true multimedia client. IEEE MultiMedia, vol. 12, no. 2, p. 104, Apr. 2005, doi: 10.1109/MMUL.2005.20 | uk_UK |
| dc.relation.referencesen | Hazar, M. J. et al. A Recommendation System Involving a Hybrid Approach of Student Review and Rating for an Educational Video. In Review, preprint, Feb. 2023. doi: 10.21203/rs.3.rs-2375194/v1 | uk_UK |
| Розташовується у зібраннях: | Наукові роботи (FITM) | |
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