Dynamic Pedagogical Resource Recommendation System for Students Using Rising Star Ranking Algorithm
DOI:
https://doi.org/10.3991/itdaf.v3i4.59211Keywords:
Machine Learning, Recommendation system, E-Learning, Course Recommendation SystemsAbstract
With the advancement of technology and the rapid growth of digital and electronic devices, the e-learning process has become easy and flexible. However, due to the lack of relevant and contextually linked learning resources, a problem may arise for students in their academics compared to physical learning, where the teacher is responsible for providing learning resources. Not only this, but there exist many other problems, like the privacy of course contents, and less availability of relevant learning material has also been responsible for the low assessment scores of students. In all situations, a need exists to provide quick, relevant, and user-friendly accessible content to the students at their doorsteps. In this study, a machine-learning technique has been designed to recommend relevant content in an e-learning environment. This work utilizes innovative machine learning-based adaptive course content to strengthen ‘students’ capabilities. The proposed method consists of data preprocessing, feature extraction through VISUWORDS, feature reduction through formal concept analysis (FCA), and finally, ranked material using the Rising Star algorithm. The experimental evaluation shows that the proposed technique offers a better turnout in accuracy (87%) than the existing benchmark methods. Last but not least, the proposed technique has also been analyzed to validate student performance through pre-posttests that show the remarkable performance of the f-score.
References
References
[1] S. Tahir, Y. Hafeez, M. A. Abbas, A. Nawaz, and B. Hamid, “Smart Learning Objects Retrieval for E-Learning with Contextual Recommendation based on Collaborative Filtering,” Educ. Inf. Technol., pp. 1–38, 2022.
[2] I. A. Mastan, D. I. Sensuse, R. R. Suryono, and K. Kautsarina, “Evaluation of distance learning system (e-learning): a systematic literature review,” J. Teknoinfo, vol. 16, no. 1, pp. 132–137.
[3] A. S. Al-Adwan, M. Nofal, H. Akram, N. A. Albelbisi, and M. Al-Okaily, “Towards a Sustainable Adoption of E-Learning Systems: The Role of Self-Directed Learning.,” J. Inf. Technol. Educ. Res., vol. 21.
[4] V. Lin, G.-Z. Liu, G.-J. Hwang, N.-S. Chen, and C. Yin, “Outcomes-based appropriation of context-aware ubiquitous technology across educational levels,” Interact. Learn. Environ., vol. 30, no. 8, pp. 1515–1538, 2022.
[5] K. Bauman and A. Tuzhilin, “Know thy context: parsing contextual information from user reviews for recommendation purposes,” Inf. Syst. Res., vol. 33, no. 1, pp. 179–202, 2022.
[6] M. Etemadi et al., “A systematic review of healthcare recommender systems: Open issues, challenges, and techniques,” Expert Syst. Appl., vol. 213, p. 118823, 2023.
[7] G.-J. Hwang, C.-C. Tsai, and S. J. H. Yang, “Criteria, strategies and research issues of context-aware ubiquitous learning,” J. Educ. Technol. & Soc., vol. 11, no. 2, pp. 81–91, 2008.
[8] H. Papadakis, A. Papagrigoriou, C. Panagiotakis, E. Kosmas, and P. Fragopoulou, “Collaborative filtering recommender systems taxonomy,” Knowl. Inf. Syst., vol. 64, no. 1, pp. 35–74, 2022.
[9] D. B. Guruge, R. Kadel, and S. J. Halder, “The state of the art in methodologies of course recommender systems—a review of recent research,” Data, vol. 6, no. 2, pp. 1–30, 2021, doi: 10.3390/data6020018.
[10] W. Al-Rahmi, A. Aldraiweesh, N. Yahaya, Y. Bin Kamin, and A. M. Zeki, “Massive open online courses (MOOCs): Data on higher education,” Data Br., vol. 22, pp. 118–125, 2019.
[11] K. K. Jena et al., “E-Learning Course Recommender System Using Collaborative Filtering Models,” Electron., vol. 12, no. 1, 2023, doi: 10.3390/electronics12010157.
[12] G.-J. Hwang, “Definition, framework and research issues of smart learning environments - a context-aware ubiquitous learning perspective,” Smart Learn. Environ., vol. 1, no. 1, p. 4, 2014, doi: 10.1186/s40561-014-0004-5.
[13] “Sharing Knowledge and Building Communities - OpenStax CNX.” https://cnx.org/ (accessed Sep. 25, 2021).
[14] T. Ali and S. Asghar, “Multi-label scientific document classification,” J. Internet Technol., vol. 19, no. 6, pp. 1707–1716, 2018.
[15] S. Vogrinčič and Z. Bosnić, “Ontology-based multi-label classification of economic articles,” Comput. Sci. Inf. Syst., vol. 8, no. 1, pp. 101–119, 2011, doi: 10.2298/CSIS100420034V.
[16] S. Ali, Y. Hafeez, M. Humayun, N. S. M. Jamail, M. Aqib, and A. Nawaz, “Enabling recommendation system architecture in virtualized environment for e-learning,” Egypt. Informatics J., vol. 23, no. 1, pp. 33–45, 2022.
[17] S. Caspari-Sadeghi, “Learning assessment in the age of big data: Learning analytics in higher education,” Cogent Educ., vol. 10, no. 1, p. 2162697, 2023.
[18] G. Abdelrahman, Q. Wang, and B. Nunes, “Knowledge tracing: A survey,” ACM Comput. Surv., vol. 55, no. 11, pp. 1–37, 2023.
[19] T. Wu, F. Sun, J. Dong, Z. Wang, and Y. Li, “Context-aware session recommendation based on recurrent neural networks,” Comput. Electr. Eng., vol. 100, p. 107916, 2022.
[20] K. Butler-Henderson and J. Crawford, “A systematic review of online examinations: A pedagogical innovation for scalable authentication and integrity,” Comput. Educ., vol. 159, no. May, p. 104024, 2020, doi: 10.1016/j.compedu.2020.104024.
[21] A. E. Clark, H. Nong, H. Zhu, and R. Zhu, “Compensating for academic loss: Online learning and student performance during the COVID-19 pandemic,” China Econ. Rev., vol. 68, 2021, doi: 10.1016/j.chieco.2021.101629.
[22] T. Purwoningsih, H. B. Santoso, and Z. A. Hasibuan, “Online Learners’ Behaviors Detection Using Exploratory Data Analysis and Machine Learning Approach,” Proc. 2019 4th Int. Conf. Informatics Comput. ICIC 2019, no. Icic, pp. 1–8, 2019, doi: 10.1109/ICIC47613.2019.8985918.
[23] O. El Aissaoui, Y. E. A. El Madani, L. Oughdir, and Y. El Allioui, “Combining supervised and unsupervised machine learning algorithms to predict the learners’ learning styles,” Procedia Comput. Sci., vol. 148, pp. 87–96, 2019, doi: 10.1016/j.procs.2019.01.012.
[24] S. S. Khanal, P. W. C. Prasad, A. Alsadoon, and A. Maag, “A systematic review: machine learning based recommendation systems for e-learning,” Educ. Inf. Technol., vol. 25, pp. 2635–2664, 2020.
[25] F. Rasheed and A. Wahid, “Learning style detection in E-learning systems using machine learning techniques,” Expert Syst. Appl., vol. 174, p. 114774, 2021.
[26] R. Alshabandar, A. Hussain, R. Keight, and W. Khan, “Students performance prediction in online courses using machine learning algorithms,” in 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1–7.
[27] M. Maksud and N. Ahmad, “Machine Learning Approaches to Digital Learning Performance Analysis,” … J. Comput. Digit. …, 2020.
[28] Y. Abubakar, N. Bahiah, and H. Ahmad, “Prediction of Students’ Performance in E-Learning Environment Using Random Forest,” Int. J. Innov. Comput., vol. 7, no. 2, pp. 1–5, 2017.
[29] M. Hussain, W. Zhu, W. Zhang, and S. M. R. Abidi, “Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores,” Comput. Intell. Neurosci., vol. 2018, 2018, doi: 10.1155/2018/6347186.
[30] Ş. Aydoğdu, “Predicting student final performance using artificial neural networks in online learning environments,” Educ. Inf. Technol., vol. 25, no. 3, pp. 1913–1927, 2020, doi: 10.1007/s10639-019-10053-x.
[31] S. Rakic, N. Tasic, U. Marjanovic, S. Softic, E. Lüftenegger, and I. Turcin, “Student performance on an e-learning platform: Mixed method approach,” Int. J. Emerg. Technol. Learn., vol. 15, no. 2, pp. 187–203, 2020, doi: 10.3991/ijet.v15i02.11646.
[32] S. S. Khanal, P. W. C. Prasad, A. Alsadoon, and A. Maag, “A systematic review: machine learning based recommendation systems for e-learning,” Educ. Inf. Technol., vol. 25, no. 4, pp. 2635–2664, 2020, doi: 10.1007/s10639-019-10063-9.
[33] S. Chavhan, M. M. Raghuwanshi, and R. C. Dharmik, “Information Retrieval using Machine learning for Ranking: A Review,” J. Phys. Conf. Ser., vol. 1913, no. 1, 2021, doi: 10.1088/1742-6596/1913/1/012150.
[34] T. Panagiotakopoulos, S. Kotsiantis, S. Borotis, F. Lazarinis, and A. Kameas, “Applying Machine Learning to Predict Whether Learners Will Start a MOOC After Initial Registration,” in IFIP International Conference on Artificial Intelligence Applications and Innovations, 2021, pp. 466–475.
[35] E. Alqurashi, “Predicting student satisfaction and perceived learning within online learning environments,” Distance Educ., vol. 40, no. 1, pp. 133–148, 2019.
[36] L. Pang, J. Xu, Q. Ai, Y. Lan, X. Cheng, and J. Wen, “Setrank: Learning a permutation-invariant ranking model for information retrieval,” in Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, 2020, pp. 499–508.
[37] “www.visuwords.com.” https://visuwords.com/ (accessed Dec. 22, 2023).
[38] Y. Yang, F. Hao, B. Pang, G. Min, and Y. Wu, “Dynamic maximal cliques detection and evolution management in social internet of things: A formal concept analysis approach,” IEEE Trans. Netw. Sci. Eng., vol. 9, no. 3, pp. 1020–1032, 2021.
[39] Y. Wan and L. Zou, “An Efficient Algorithm for Decreasing the Granularity Levels of Attributes in Formal Concept Analysis,” IEEE Access, vol. 7, pp. 11029–11040, 2019, doi: 10.1109/ACCESS.2019.2892016.
[40] S. Bhaskaran and R. Marappan, “Enhanced personalized recommendation system for machine learning public datasets: generalized modeling, simulation, significant results and analysis,” Int. J. Inf. Technol., vol. 15, no. March, pp. 1583–1595, 2023, doi: 10.1007/s41870-023-01165-2.
[41] M. Q. Zia, M. A. Bashir, R. A. Mangi, and A. F. Shamsi, “A person-situation perspective of informal learning: the role of supervisor feedback environment,” Eur. J. Train. Dev., vol. 46, no. 1–2, pp. 120–138, 2022, doi: 10.1108/EJTD-09-2020-0142.
[42] X. Wang, X. Yu, L. Guo, F. Liu, and L. Xu, “Student performance prediction with short-term sequential campus behaviors,” Information, vol. 11, no. 4, p. 201, 2020.
[43] “MIT - Massachusetts Institute of Technology.” https://www.mit.edu/ (accessed Dec. 22, 2023).
[44] “PMAS-Arid Agriculture University Rawalpindi.” https://www.uaar.edu.pk/index.php (accessed Dec. 22, 2023).
[45] L. A. Burgess, “WebCT as an e-learning tool: A study of technology students’ perceptions,” 2003.
[46] Z. Shahbazi and Y.-C. Byun, “Agent-based recommendation in E-learning environment using knowledge discovery and machine learning approaches,” Mathematics, vol. 10, no. 7, p. 1192, 2022.
[47] F. Saleem, Z. Ullah, B. Fakieh, and F. Kateb, “Intelligent decision support system for predicting student’s E-learning performance using ensemble machine learning,” Mathematics, vol. 9, no. 17, p. 2078, 2021.
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Copyright (c) 2025 Anam Zaheer, Sidra Tahir, asif Nawaz, ahthasham Sajid, Muhammad Zeeshan, Sabitha Banu
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