Online learning platforms are used to discover the optimal learning courses for learners according to their interests and knowledge. An effective methodology is needed to suggest effective learning courses according to Sentiment Analysis (SA). It is difficult to handle large user feedback manually, so the recommendation system is utilized. The recommender system should be developed with high efficiency in filtering information. Also, it requires efficient access and resolves the issue of information overload. In order to solve this issue, effective deep learning-based approaches for online course ranking were presented in this paper. The input dataset used for this work contains the information on the online course. Initially, the input text data is pre- processed using different effective pre-processing approaches. Afterwards, features such as Improved Term Frequency-Inverse Document Frequency (ITF-IDF), Bag of Words (BoW), and glove word embedding are extracted to enhance the classification performance. Further, the Modified Rain Optimization (MRO) algorithm is utilized for feature selection by reducing the redundant features. Finally, an Improved Deep active Convolutional Neural Network (IDCNN) is presented for online course preference SA. Here, the Adaptive Beetle Antennae optimization algorithm (ABA) is utilized for weight optimization in the proposed IDCNN. This enhanced IDCNN predicts effective online courses by using SA as positive, negative, and neutral. Finally, the optimal learning course ranking is performed through the Jaccard similarity approach. This final recommendation through ranking improves the quality of the selection process for an online course. The presented methodology is implemented in the Python programming language. The experimental results proved that the presented approach attains enhanced performance on different performances like accuracy (98.17%), precision (98.23%), F1-score (98.19%), Root Mean Squared Error (RMSE) (0.21), Kappa (97.06%), recall (98.21%), and Area Under the Curve (AUC) (98.17%).

[1] Afsar M., Crump T., and Far B., “Reinforcement Learning Based Recommender Systems: A Survey,” ACM Computing Surveys, vol. 55, no. 7, pp. 1-38, 2022. https://doi.org/10.1145/3543846

[2] Ali S., Hafeez Y., Humayun M., Jamail N., Aqib M., and Nawaz A., “Enabling Recommendation System Architecture in Virtualized Environment for E-Learning,” Egyptian Informatics Journal, vol. 23, no. 1, pp. 33-45, 2022. https://doi.org/10.1016/j.eij.2021.05.003

[3] Almusharraf N. and Khahro S., “Students Satisfaction with Online Learning Experiences during the COVID-19 Pandemic,” International Journal of Emerging Technologies in Learning, vol. 15, no. 21, pp. 246-267, 2020. https://doi.org/10.3991/ijet.v15i21.15647

[4] Alojaiman B., “Toward Selection of Trustworthy and Efficient E-Learning Platform,” IEEE Access, vol. 9, pp. 133889-133901, 2021. DOI: 10.1109/ACCESS.2021.3114150

[5] Antony Rosewelt L. and Arokia Renjit J., “A Content Recommendation System for Effective E- Learning Using Embedded Feature Selection and Fuzzy DT Based CNN,” Journal of Intelligent and Fuzzy Systems, vol. 39, no. 1, pp. 795-808, 2020. https://doi.org/10.3233/JIFS-191721

[6] Ayyub K., Iqbal S., Munir E., Nisar M., and Abbasi M., “Exploring Diverse Features for Sentiment Quantification Using Machine Learning Algorithms,” IEEE Access, vol. 8, pp. 142819-142831, 2020. DOI: 10.1109/ACCESS.2020.3011202

[7] Birjali M., Beni-Hssane A., and Erritali M., “A Novel Adaptive E-Learning Model Based on Big Data by Using Competence-based Knowledge and Social Learner Activities,” Applied Soft Computing, vol. 69, pp. 14-32, 2018. https://doi.org/10.1016/j.asoc.2018.04.030

[8] Bouazizi E., Khedr A., and Idrees A., “Leveraging Chicken Swarm Algorithm for Feature Selection Optimization Targeting Efficient Patients Backlog Elimination,” The International Arab Journal of Information Technology, vol. 22, no. 2, pp. 364- 376 2025. https://doi.org/10.34028/iajit/22/2/12 An Effective Online Learning Course Recommendation Using Improved Deep Active ... 785

[9] Chrysafiadi K., Troussas C., and Virvou M., “Combination of Fuzzy and Cognitive Theories for Adaptive E-Assessment,” Expert Systems with Applications, vol. 161, pp. 113614, 2020. https://doi.org/10.1016/j.eswa.2020.113614

[10] Clarizia F., Colace F., De Santo M., Lombardi M., Pascale F., and Pietrosanto A., “E-Learning and Sentiment Analysis: A Case Study,” in Proceedings of the 6th International Conference on Information and Education Technology, Osaka, pp. 111-118, 2018. https://doi.org/10.1145/3178158.3178181

[11] Curum B. and Khedo K., “Cognitive Load Management in Mobile Learning Systems: Principles and Theories,” Journal of Computers in Education, vol. 8, no. 1, pp. 109-136, 2021. https://doi.org/10.1007/s40692-020-00173-6

[12] Dang C., Moreno-Garcia M., and Prieta F., “An Approach to Integrating Sentiment Analysis into Recommender Systems,” Sensors, vol. 21, no. 16, pp. 1-17, 2021. DOI: 10.3390/s21165666

[13] Davis D., Jivet I., Kizilcec R., Chen G., Hauff C., and Houben G., “Follow the Successful Crowd: Raising MOOC Completion Rates through Social Comparison at Scale,” in Proceedings of the 7th International Learning Analytics and Knowledge Conference, Vancouver, pp. 454-463, 2017. https://doi.org/10.1145/3027385.3027411

[14] Dessi D., Dragoni M., Fenu G., Marras M., and Recupero D., “Evaluating Neural Word Embeddings Created from Online Course Reviews for Sentiment Analysis,” in Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, Limassol, pp. 2124-2127, 2019. https://doi.org/10.1145/3297280.3297620

[15] Esparza G., De-Luna A., Zezzatti A., Hernandez A., Ponce J., Alvarez M., Cossio E., and De Jesus Nava J., “A Sentiment Analysis Model to Analyze Students Reviews of Teacher Performance Using Support Vector Machines,” in Proceedings of the 14th International Conference on Distributed Computing and Artificial Intelligence, Porto, pp. 157-164, 2017. https://doi.org/10.1007/978-3- 319-62410-5_19

[16] Gund A., Lo H., Chiu C., and Cheng C., “Sentiment Analysis and Summarization of Restaurant Reviews Using T5 and ChatGPT,” SSRN, pp. 1-17, 2024. https://papers.ssrn.com/sol3/papers.cfm?abstract_ id=4963924

[17] Hao P., Li Y., and Bai C., “Meta-Relationship for Course Recommendation in MOOCs,” Multimedia Systems, vol. 29, no. 1, pp. 235-246, 2023. https://doi.org/10.1007/s00530-022-00989- 5

[18] Hasan M., Sohaib M., and Kim J., “An Explainable AI-based Fault Diagnosis Model for Bearings,” Sensors, vol. 21, no. 12, pp. 1-34, 2021. https://doi.org/10.3390/s21124070

[19] Hemanth M., GitHub, https://github.com/itsmehemm/E-learning- Recommender-System, Last Visited, 2024.

[20] Hew K., Hu X., Qiao C., and Tang Y., “What Predicts Student Satisfaction with MOOCs: A Gradient Boosting Trees Supervised Machine Learning and Sentiment Analysis Approach,” Computers and Education, vol. 145, pp. 103724, 2020. https://doi.org/10.1016/j.compedu.2019.103724

[21] Hoq M., “E-Learning during the Period of Pandemic (COVID-19) in the Kingdom of Saudi Arabia: An Empirical Study,” American Journal of Educational Research, vol. 8, no. 7, pp. 457- 464, 2020. https://www.sciepub.com/journal/education

[22] Huang C., Han Z., Li M., Jong M., and Tsai C., “Investigating Students’ Interaction Patterns and Dynamic Learning Sentiments in Online Discussions,” Computers and Education, vol. 140, pp. 103589, 2019. https://doi.org/10.1016/j.compedu.2019.05.015

[23] Huang S., Liu Q., Chen J., Hu X., Liu Z., and Luo W., “A Design of a Simple Yet Effective Exercise Recommendation System in K-12 Online Learning,” in Proceedings of the 23rd International Conference on Artificial Intelligence in Education Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, Durham, pp. 208-212, 2022. https://doi.org/10.1007/978-3-031-11647- 6_36

[24] Imran A. and Cheikh F., “Multimedia Learning Objects Framework for E-Learning,” in Proceedings of the International Conference on E- Learning and E-Technologies in Education, pp. 105-109, Lodz, 2012. DOI: 10.1109/ICeLeTE.2012.6333417

[25] Iwendi C., Ibeke E., Eggoni H., Velagala S., and Srivastava G., “Pointer-based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model,” International Journal of Information Technology and Decision Making, vol. 21, no. 1, pp. 463-484, 2022. https://doi.org/10.1142/S0219622021500619

[26] Karabila I., Darraz N., EL-Ansari A., Alami N., and EL Mallahi M., “BERT-Enhanced Sentiment Analysis for Personalized E-Commerce Recommendations,” Multimedia Tools and Applications, vol. 83, no. 19, pp. 56463-56488, 2024. https://doi.org/10.1007/s11042-023-17689- 5

[27] Kastrati Z., Imran A., and Kurti A., “Weakly Supervised Framework for Aspect-based Sentiment Analysis on Students’ Reviews of MOOCs,” IEEE Access, vol. 8, pp. 106799- 786 The International Arab Journal of Information Technology, Vol. 22, No. 4, July 2025 106810, 2020. DOI: 10.1109/ACCESS.2020.3000739

[28] Katragadda S., Ravi V., Kumar P., and Lakshmi G., “Performance Analysis on Student Feedback Using Machine Learning Algorithms,” in Proceedings of the 6th International Conference on Advanced Computing and Communication Systems, Coimbatore, pp. 1161-1163, 2020. DOI: 10.1109/ICACCS48705.2020.9074334

[29] Khalid A., Lundqvist K., Yates A., and Ghzanfar M., “Novel Online Recommendation Algorithm for Massive Open Online Courses (NoR- MOOCs),” PloS One, vol. 16, no. 1, pp. 1-21, 2021. DOI: 10.1371/journal.pone.0245485

[30] Khanal S., Prasad P., Alsadoon A., and Maag A., “A Systematic Review: Machine Learning Based Recommendation Systems for E-Learning,” Education and Information Technologies, vol. 25, no. 4, pp. 2635-2664, 2020. https://doi.org/10.1007/s10639-019-10063-9

[31] Krouska A., Troussas C., and Sgouropoulou C., “Applying Genetic Algorithms for Recommending Adequate Competitors in Mobile Game-based Learning Environments,” in Proceedings of the 16th International Conference on Intelligent Tutoring Systems, Athens, pp. 196- 204, 2020. https://doi.org/10.1007/978-3-030- 49663-0_23

[32] Krouska A., Troussas C., and Sgouropoulou C., “Mobile Game-based Learning as a Solution in COVID-19 Era: Modeling the Pedagogical Affordance and Student Interactions,” Education and Information Technologies, vol. 27, no. 1, pp. 229-241, 2022. https://doi.org/10.1007/s10639- 021-10672-3

[33] Lemay D. and Doleck T., “Predicting Completion of Massive Open Online Course (MOOC) Assignments from Video Viewing Behavior,” Interactive Learning Environments, vol. 30, no. 10, pp. 1782-1793, 2022. https://doi.org/10.1080/10494820.2020.1746673

[34] Li G. and Wang F., “Image Object and Scene Recognition Based on Improved Convolutional Neural Network,” The International Arab Journal of Information Technology, vol. 21, no. 5, pp. 925- 937, 2024. https://doi.org/10.34028/iajit/21/5/13

[35] Li J. and Ye Z., “Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm,” Complexity, vol. 2020, no. 1, pp. 1-10, 2020. https://doi.org/10.1155/2020/6619249

[36] Liu Z., Liu S., Liu L., Sun J., Peng X., and Wang T., “Sentiment Recognition of Online Course Reviews Using Multi-Swarm Optimization-based Selected Features,” Neurocomputing, vol. 185, pp. 11-20, 2016. https://doi.org/10.1016/j.neucom.2015.12.036

[37] Lwin H., Oo S., Ye K., Lin K., Aung W., and Ko P., “Feedback Analysis in Outcome Base Education Using Machine Learning,” in Proceedings of the 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Phuket, pp. 767-770, 2020. DOI: 10.1109/ECTI-CON49241.2020.9158328

[38] Madani Y., Ezzikouri H., Erritali M., and Hssina B., “Finding Optimal Pedagogical Content in an Adaptive E-Learning Platform Using a New Recommendation Approach and Reinforcement Learning,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, pp. 3921-3936, 2020. https://doi.org/10.1007/s12652-019-01627- 1

[39] Maleki N., Zeinali Y., and Niaki S., “A K-NN Method for Lung Cancer Prognosis with the Use of a Genetic Algorithm for Feature Selection,” Expert Systems with Applications, vol. 164, pp. 113981, 2021. https://doi.org/10.1016/j.eswa.2020.113981

[40] Maree M., Eleyat M., and Mesqali E., “Optimizing Machine Learning-based Sentiment Analysis Accuracy in Bilingual Sentences via Preprocessing Techniques,” The International Arab Journal of Information Technology, vol. 21, no. 2, pp. 257-270, 2024. https://doi.org/10.34028/iajit/21/2/8

[41] Moazzeni A. and Khamehchi E., “Rain Optimization Algorithm (ROA): A New Metaheuristic Method for Drilling Optimization Solutions,” Journal of Petroleum Science and Engineering, vol. 195, pp. 107512, 2020. https://doi.org/10.1016/j.petrol.2020.107512

[42] Nanda E., Sowmya M., Khatua L., Teja P., and Deshmukh P., “ALBERT-based Personalized Educational Recommender System: Enhancing Students’ Learning Outcomes in Online Learning,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 10, pp. 2190-2201, 2023. https://doi.org/10.17762/ijritcc.v11i10.8906

[43] Pashaei E. and Pashaei E., “Hybrid Binary Arithmetic Optimization Algorithm with Simulated Annealing for Feature Selection in High-Dimensional Biomedical Data,” The Journal of Supercomputing, vol. 78, no. 13, pp. 15598-15637, 2022. https://doi.org/10.1007/s11227-022-04507-2

[44] Pustokhina I., Pustokhin D., Nguyen P., Elhoseny M., and Shankar K., “Multi-Objective rain Optimization Algorithm with WELM Model for Customer Churn Prediction in Telecommunication Sector,” Complex and Intelligent Systems, pp. 1-13, 2021. DOI:10.1007/s40747-021-00353-6

[45] Rafiq M., Jianshe X., Arif M., and Barra P., An Effective Online Learning Course Recommendation Using Improved Deep Active ... 787 “Intelligent Query Optimization and Course Recommendation during Online Lectures in E- Learning System,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 11, pp. 10375-10394, 2021. https://doi.org/10.1007/s12652-020-02834-x

[46] Rodrigues H., Almeida F., Figueiredo V., and Lopes S., “Tracking E-Learning through Published Papers: A Systematic Review,” Computers and Education, vol. 136, pp. 87-98, 2019. https://doi.org/10.1016/j.compedu.2019.03.007

[47] Rostami M., Forouzandeh S., Berahmand K., and Soltani M., “Integration of Multi-Objective PSO Based Feature Selection and Node Centrality for Medical Datasets,” Genomics, vol. 112, no. 6, pp. 4370-4384, 2020. https://doi.org/10.1016/j.ygeno.2020.07.027

[48] Tarus J., Niu Z., and Kalui D., “A Hybrid Recommender System for E-Learning Based on Context Awareness and Sequential Pattern Mining,” Soft Computing, vol. 22, pp. 2449-2461, 2018. https://doi.org/10.1007/s00500-017-2720-6

[49] Troussas C., Krouska A., Alepis E., and Virvou M., “Intelligent and Adaptive Tutoring through a Social Network for Higher Education,” New Review of Hypermedia and Multimedia, vol. 26, no. 3-4, pp. 138-167, 2020. https://doi.org/10.1080/13614568.2021.1908436

[50] Troussas C., Krouska A., and Sgouropoulou C., “Improving Learner-Computer Interaction through Intelligent Learning Material Delivery Using Instructional Design Modeling,” Entropy, vol. 23, no. 6, pp. 1-11, 2021. https://doi.org/10.3390/e23060668

[51] Vedavathi N. and Anil Kumar K., “An Efficient E-Learning Recommendation System for User Preferences Using Hybrid Optimization Algorithm,” Soft Computing, vol. 25, no. 14, pp. 9377-9388, 2021. https://doi.org/10.1007/s00500- 021-05753-x

[52] Wang W., Guo L., He L., and Wu Y., “Effects of Social-Interactive Engagement on the Dropout Ratio in Online Learning: Insights from MOOC,” Behaviour and Information Technology, vol. 38, no. 6, pp. 621-636, 2019. https://doi.org/10.1080/0144929X.2018.1549595

[53] Wang Y. and Li T., “Local Feature Selection Based on Artificial Immune System for Classification,” Applied Soft Computing, vol. 87, pp. 105989, 2020. https://doi.org/10.1016/j.asoc.2019.105989

[54] Wang Y., Zhang W., Ma J., and Jin Q., “ADCB: Adaptive Dynamic Clustering of Bandits for Online Recommendation System,” Neural Processing Letters, vol. 55, no. 2, pp. 1155-1172, 2023. https://doi.org/10.1007/s11063-022-10931- 5

[55] Yang R., “Machine Learning and Deep Learning for Sentiment Analysis over Students’ Reviews: An Overview Study,” Preprints, vol. 2021020108.v1, pp. 1-9, 2021. DOI:10.20944/preprints202102.0108.v1

[56] Yu J., Yin H., Xia X., Chen T., Li J., and Huang Z., “Self-Supervised Learning for Recommender Systems: A Survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 1, pp. 335-355, 2024. DOI: 10.1109/TKDE.2023.3282907

[57] Zeng J., Luo K., Lu Y., and Wang M., “An Evaluation Framework for Online Courses Based on Sentiment Analysis Using Machine Learning,” International Journal of Emerging Technologies in Learning, vol. 18, no. 18, pp. 4-22, 2023. https://doi.org/10.3991/ijet.v18i18.42521