E-Learning influences on student learning satisfaction levels in terms of learner’s personalization
Abstract
This study examines student sentiment about their online classroom communities in terms of learner’s satisfaction when they are a combination of asynchronous and synchronous courses on the Internet. The results show that the design of an e-learning system is aimed at improving learners’ sense of connection in the virtual classroom. In particular, when creating the e-learning system, attention should be given to user experience, communication, organizing content, and personalization. In addition, a new evaluation technique based on machine learning (ML) has been proposed for evaluation through e-learning programs. Support Vector Machine (SVM), Neural Networks (NN), and Decision Trees (DT) are three ML techniques that are combined with multiple linear regressions to create prediction models as discussed with connectedness and learning for identifying the underlying relationships between the important digital to an e-Learning method and its estimator variables. The suitability of the rank-order forecast is assessed based on the susceptibility analysis. A metric is developed using both the usability ratings and the susceptible levels. The intensity index values are ordered and the most crucial usage patterns are found using a methodology similar to Pareto.
Keywords
Full Text:
PDFReferences
1. Sayed WS, Noeman AM, Abdellatif A, et al. AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging E-learning platform. Multimedia Tools and Applications. 2022; 82(3): 3303-3333. doi: 10.1007/s11042-022-13076-8
2. Rahayu NW, Ferdiana R, Kusumawardani SS. A systematic review of ontology use in E-Learning recommender system. Computers and Education: Artificial Intelligence. 2022; 3: 100047. doi: 10.1016/j.caeai.2022.100047
3. Serey J, Quezada L, Alfaro M, et al. Artificial Intelligence Methodologies for Data Management. Symmetry. 2021; 13(11): 2040. doi: 10.3390/sym13112040
4. Moustakas L, Robrade D. The Challenges and Realities of E-Learning during COVID-19: The Case of University Sport and Physical Education. Challenges. 2022; 13(1): 9. doi: 10.3390/challe13010009
5. Rajakumar G, Du KL, Vuppalapati C, et al. Intelligent Communication Technologies and Virtual Mobile Networks. Springer Nature Singapore; 2023. doi: 10.1007/978-981-19-1844-5
6. Prasad VN, Kureekatil Muthappa AK. An efficient framework for the similarity prediction with query recommendation in E‐learning system. Concurrency and Computation: Practice and Experience. 2022; 34(22). doi: 10.1002/cpe.7145
7. Angeline DMD, Ramasubramanian P, James I SP, et al. Identification of learners’ emotions in a learning environment using naïve bayes algorithm and evaluation of academic achivement with random forest al. International Journal of Information Retrieval Research. 2022; 12(1): 1-16. doi: 10.4018/ijirr.300341
8. Venkatesh M, Sathyalaksmi S. Memetic swarm clustering with deep belief network model for e‐learning recommendation system to improve learning performance. Concurrency and Computation: Practice and Experience. 2022; 34(18). doi: 10.1002/cpe.7010
9. Gunda C, Maddelabanda M, Shanmugasundaram H. Free Hand Text Displaying Through Hand Gestures Using MediaPipe. 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT). Published online August 11, 2022. doi: 10.1109/icicict54557.2022.9917991
10. Baig MI, Shuib L, Yadegaridehkordi E. E-learning adoption in higher education: A review. Information Development. 2021; 38(4): 570-588. doi: 10.1177/02666669211008224
11. Jeevamol J, Renumol VG. An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem. Education and Information Technologies. Published online March 30, 2021. doi: 10.1007/s10639-021-10508-0
12. Joy J, Pillai RVG. Review and classification of content recommenders in E-learning environment. Journal of King Saud University - Computer and Information Sciences. 2022; 34(9): 7670-7685. doi: 10.1016/j.jksuci.2021.06.009
13. Wahyono ID, Saryono D, Putranto H, et al. Shared Nearest Neighbour in Text Mining for Classification Material in Online Learning Using Mobile Application. International Journal of Interactive Mobile Technologies (iJIM). 2022; 16(04): 159-168. doi: 10.3991/ijim.v16i04.28991
14. Daneshvar A, Homayounfar M, Fadaei Eshkiki M, Doshmanziari E. Developing a Model for Performance Evaluation of Teachers in Electronic Education System Using Adaptive Neuro Fuzzy Inference System (ANFIS). Journal of New Approaches in Educational Administration. 2021, 12(4): 176-190.
15. Kartel A, Charles M, Xiao H, et al. Strategies for Parent Involvement During Distance Learning in Arabic Lessons in Elementary Schools. Journal International of Lingua and Technology. 2022; 1(2): 99-113. doi: 10.55849/jiltech.v1i2.82
16. Aalam SW, Ahanger AB, Bhat MR, Assad A. Evaluation of Fairness in Recommender Systems: A Review. In International Conference on Emerging Technologies in Computer Engineering. Springer, Cham; 2022.
17. Ahanger AB, Aalam SW, Bhat MR, Assad A. Popularity Bias in Recommender Systems-A Review. In International Conference on Emerging Technologies in Computer Engineering. Springer, Cham; 2022.
18. Prasad VN, Kureekatil Muthappa AK. An efficient framework for the similarity prediction with query recommendation in E‐learning system. Concurrency and Computation: Practice and Experience. 2022; 34(22). doi: 10.1002/cpe.7145
19. Zhang H. Recommendation Method of Online Teaching Resources in Colleges and Universities Considering Different User Preference Factors. Qu Z, ed. Wireless Communications and Mobile Computing. 2022; 2022: 1-9. doi: 10.1155/2022/7276495
20. Shahbazi Z, Byun YC. Agent-Based Recommendation in E-Learning Environment Using Knowledge Discovery and Machine Learning Approaches. Mathematics. 2022; 10(7): 1192. doi: 10.3390/math10071192
21. Aslam SM, Jilani AK, Sultana J, et al. Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey. IEEE Access. 2021; 9: 69573-69587. doi: 10.1109/access.2021.3077663
22. Rajeswari J, Hariharan S. Personalized Search Recommender System: State of Art, Experimental Results and Investigations. International Journal of Education and Management Engineering. 2016; 6(3): 1-8. doi: 10.5815/ijeme.2016.03.01
23. Shen Y, Wu J. English Teaching Ability Evaluation Algorithm Based on Random Matrix Model and Fuzzy K-Means Clustering. Cao N, ed. Mathematical Problems in Engineering. 2022; 2022: 1-11. doi: 10.1155/2022/7617169
24. Siddaiah SK, PM DMS. Technique to Predict Student Performance Through Ensemble Learning Algorithm in E-Learning Environment. Available at SSRN 4054489.
25. Joy J, Pillai RVG. Review and classification of content recommenders in E-learning environment. Journal of King Saud University-Computer and Information Sciences. 2022; 34(9): 7670-7685. doi: 10.1016/j.jksuci.2021.06.009
26. Kuznetsov S, Kordík P. Improving recommendation diversity and serendipity with an Ontology-based algorithm for cold start environments. Published online July 13, 2022. doi: 10.21203/rs.3.rs-1813293/v1
27. Thoo AC, Hang SP, Lee YL, et al. Students’ Satisfaction Using E-Learning as a Supplementary Tool. International Journal of Emerging Technologies in Learning (IJET). 2021; 16(15): 16. doi: 10.3991/ijet.v16i15.23925
28. Ivanytska N, Dovhan L, Tymoshchuk N, et al. Assessment of Flipped Learning as an Innovative Method of Teaching English: A Case Study. Published online January 7, 2022. doi: 10.31235/osf.io/vtz5h
29. Toktamysov S, Alwaely SA, Gallyamova Z. Digital technologies in history training: the impact on students` academic performance. Education and Information Technologies. 2022; 28(2): 2173-2186. doi: 10.1007/s10639-022-11210-5
30. Tahir S, Hafeez Y, Abbas MA, et al. Smart Learning Objects Retrieval for E-Learning with Contextual Recommendation based on Collaborative Filtering. Education and Information Technologies. 2022; 27(6): 8631-8668. doi: 10.1007/s10639-022-10966-0
31. Hariharan S. Multi document summarization by combinational approach. International Journal of Computational Cognition. 2010, 8(4), 68-74.
32. Shi J. System Evaluation and Management of College Students’ Physical Exercise Behavior Stages Integrating Bayesian Association Rules Data Mining Algorithm. Advances in Multimedia. 2022; 2022: 1-11. doi: 10.1155/2022/1655605
33. Ezaldeen H, Misra R, Bisoy SK, et al. A hybrid E-learning recommendation integrating adaptive profiling and sentiment analysis. Journal of Web Semantics. 2022; 72: 100700. doi: 10.1016/j.websem.2021.100700
34. Kaur R, Garg A, Kaur P. Case Study: Student’s Response Towards Online Learning in Engineering Education During COVID-19 Pandemic. Journal of Engineering Education Transformations. 2021; 34(3): 62. doi: 10.16920/jeet/2021/v34i3/153917
35. Dutta R, Malhotra S, Kumar A, et al. Effect of cognition on e-learners as compared to traditional learners during Covid19. Didactic transfer of physics knowledge through distance education: DIDFYZ 2021.
36. Rajasekaran VA, Kumar K. R., Susi S., Mohan Y. C., Raju M, Hssain MW. An Evaluation of E-Learning and User Satisfaction. International Journal of Web-Based Learning and Teaching Technologies. 2021; 17(2): 1-11. doi: 10.4018/ijwltt.20220301.oa3
37. Poortavakoli A, Alinejad M, Daneshmand B. Designing a pattern for e-content development based on the factors affecting satisfaction in e-learning. Technology of education journal (TEJ). 2020, 15(1): 119-138.
38. Deshpande A, Raut R, Gupta K, et al. Predictors of continued intention of working professionals for pursuing e-learning courses for career advancement. Information Discovery and Delivery. Published online July 3, 2023. doi: 10.1108/idd-11-2022-0120
39. Bansal R, Singh R, Singh A, et al. Redefining Virtual Teaching Learning Pedagogy. John Wiley & Sons; 2023.
DOI: https://doi.org/10.32629/jai.v7i5.1208
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 M.R.M Veeramanickam, Ciro Rodriguez, Navarro Carlos, Roman Concha Ulises, Lezama Pedro, Bishwajeet Pandey
License URL: https://creativecommons.org/licenses/by-nc/4.0/