A Machine Learning Based Approach to Course and Career Recommendation System: A Systematic Literature Review
DOI:
https://doi.org/10.62411/jcta.12603Keywords:
Artificial Intelligence, Course Recommendation System, Deep Learning, Machine Learning, Recommender system, Systematic ReviewAbstract
Learners are continually faced with choosing appropriate courses or making career choices due to increased educational opportunities. The emergence of machine learning-based course and career recommender systems has the potential to address this issue, offering personalized course recommendations tailored to individual learning pathways, preferences, and learning history. The optimization and feature engineering techniques and practical deployment environments have not been collectively examined in the previous research, despite the significant advancements in this area of research. Furthermore, previous research has rarely synthesized how these technical components help students choose appropriate courses and careers. This systematic review was carried out to investigate the current state of machine learning-based course and career recommender systems, focusing on key elements, such as primary data sources, feature engineering methods, algorithms, optimization techniques, evaluation metrics, and the environments where the existing course recommendation models are deployed. The PRISMA method for conducting a systematic review was used to choose studies that met the requirements for inclusion and exclusion. The study findings show significant reliance on interpretable and traditional machine learning algorithms, such as K-Nearest Neighbor and Random Forest, to develop recommender models. Feature engineering remains basic, as most studies rely on normalization, while optimization processes are often underreported. Also, evaluation metrics varied widely, impeding comparability, while most of the recommender models are deployed in an e-learning environment, leaving the traditional learning environment underrepresented. Furthermore, the study findings identified issues including data sparsity and diversity, data security and privacy, and changes in learner preferences that may have an impact on the performance of recommender systems while recommending further studies to make use of standardized optimization methods, and automated domain-informed feature engineering frameworks, benchmark and annotated datasets in developing models the gives priority to learners’ success and educational relevance.References
J. Li and Z. Ye, “Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm,” Complexity, vol. 2020, 2020, doi: 10.1155/2020/6619249.
K. K. San, H. H. Win, K. Ei, and E. Chaw, “Enhancing Hybrid Course Recommendation with Weighted Voting Ensemble Learn-ing,” J. Futur. Artif. Intell., vol. 1, no. 4, pp. 337–347, 2025, doi: 10.62411/faith.3048-3719-55.
L. Zhong, Y. Wei, H. Yao, W. Deng, Z. Wang, and M. Tong, “Review of Deep Learning-Based Personalized Learning Recommen-dation,” in ACM International Conference Proceeding Series, 2020, pp. 145–149. doi: 10.1145/3377571.3377587.
S. Algarni and F. Sheldon, “Systematic Review of Recommendation Systems for Course Selection,” Jun. 01, 2023, MDPI. doi: 10.3390/make5020033.
J. Lin, H. Pu, Y. Li, and J. Lian, “Intelligent Recommendation System for Course Selection in Smart Education,” in Procedia Computer Science, Elsevier B.V., 2018, pp. 449–453. doi: 10.1016/j.procs.2018.03.023.
Z. Qiu, Y. Hu, and X. Wu, “Graph Neural News Recommendation with User Existing and Potential Interest Modeling,” ACM Trans. Knowl. Discov. Data, vol. 16, no. 5, Oct. 2022, doi: 10.1145/3511708.
J. R. D. Atienza, R. M. Hernandez, R. L. Castillo, N. M. De Jesus, and L. J. E. Buenas, “A Deep Neural Network in a Web-based Career Track Recommender System for Lower Secondary Education,” in 2022 2nd Asian Conference on Innovation in Technology, ASIANCON 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/ASIANCON55314.2022.9908965.
P. Verma, S. K. Sood, and S. Kalra, “Student career path recommendation in engineering stream based on three-dimensional model,” Comput. Appl. Eng. Educ., vol. 25, no. 4, pp. 578–593, 2017, doi: 10.1002/cae.21822.
P. C. Siswipraptini, H. L. H. S. Warnars, A. Ramadhan, and W. Budiharto, “Personalized Career-Path Recommendation Model for Information Technology Students in Indonesia,” IEEE Access, vol. 12, no. March, pp. 49092–49105, 2024, doi: 10.1109/ACCESS.2024.3381032.
R. A. Oguntuase, A. J. Gabriel, and B. A. Ojokoh, “A Personalized Context-Aware Places of Interest Recommender System,” J. Comput. Theor. Appl., vol. 2, no. 4, pp. 481–497, Apr. 2025, doi: 10.62411/jcta.12362.
T. Liu, Q. Wu, L. Chang, and T. Gu, “A review of deep learning-based recommender system in e-learning environments,” Artif. Intell. Rev., vol. 55, no. 8, pp. 5953–5980, Dec. 2022, doi: 10.1007/s10462-022-10135-2.
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,” Feb. 01, 2021, MDPI. doi: 10.3390/data6020018.
K. K. Jena et al., “E-Learning Course Recommender System Using Collaborative Filtering Models,” Electron., vol. 12, no. 1, Jan. 2023, doi: 10.3390/electronics12010157.
C. N. Dang, M. N. Moreno-García, and F. De la Prieta, “Using Hybrid Deep Learning Models of Sentiment Analysis and Item Genres in Recommender Systems for Streaming Services,” Electronics, vol. 10, no. 20, p. 2459, Oct. 2021, doi: 10.3390/electronics10202459.
Z. Gulzar, A. A. Leema, and G. Deepak, “PCRS: Personalized Course Recommender System Based on Hybrid Approach,” in Procedia Computer Science, Elsevier B.V., 2018, pp. 518–524. doi: 10.1016/j.procs.2017.12.067.
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, Jul. 2020, doi: 10.1007/s10639-019-10063-9.
J. Zhong, H. Xie, and F. L. Wang, “The research trends in recommender systems for e-learning,” Asian Assoc. Open Univ. J., vol. 14, no. 1, pp. 12–27, Nov. 2019, doi: 10.1108/AAOUJ-03-2019-0015.
R. Bhanuse and S. Mal, “A Systematic Review: Deep Learning based E-Learning Recommendation System,” in Proceedings - Interna-tional Conference on Artificial Intelligence and Smart Systems, ICAIS 2021, Institute of Electrical and Electronics Engineers Inc., Mar. 2021, pp. 190–197. doi: 10.1109/ICAIS50930.2021.9395835.
S. Souabi, A. Retbi, M. K. Idrissi, and S. Bennani, “Recommendation Systems on E-Learning and Social Learning: A Systematic Review,” Electron. J. e-Learning, vol. 19, no. 5, pp. 432–451, 2021, doi: 10.34190/ejel.19.5.2482.
N. Askarbekuly and I. Luković, “Learning Outcomes, Assessment, and Evaluation in Educational Recommender Systems: A Sys-tematic Review,” Jun. 2024.
S. A. Khan, S. Ali, A. Deorankar, R. Dhomble, and S. A. Shah, “A Comprehensive Survey on Machine Learning Approaches in Course Recommendation System,” in 2024 IEEE International Students’ Conference on Electrical, Electronics and Computer Science, SCEECS 2024, Institute of Electrical and Electronics Engineers Inc., 2024. doi: 10.1109/SCEECS61402.2024.10482042.
M. Phiri, A. Zimba, and C. I. Eke, “Deep Learning Techniques for Photovoltaic Solar Tracking Systems : A Systematic Literature Review,” Res. Sq., 2023, doi: doi.org/10.21203/rs.3.rs-2539961/v1.
L. Caiwen, I. Ishak, H. Ibrahim, M. Zolkepli, F. Sidi, and C. Li, “Deep Learning-Based Recommendation System: Systematic Review and Classification,” 2023, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ACCESS.2023.3323353.
H. Y. Ahmadian, S. J. S. Mahdavi, and H. A. Yazdi, “Dynamic educational recommender system based on Improved LSTM neural network,” Sci. Rep., vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-54729-y.
S. Bhaskaran and R. Marappan, “Design and analysis of an efficient machine learning based hybrid recommendation system with enhanced density-based spatial clustering for digital e-learning applications,” Complex Intell. Syst., vol. 9, no. 4, pp. 3517–3533, Aug. 2023, doi: 10.1007/s40747-021-00509-4.
M. Isma’Il, U. Haruna, G. Aliyu, I. Abdulmumin, and S. Adamu, “An Autonomous Courses Recommender System for Under-graduate Using Machine Learning Techniques,” in 2020 International Conference in Mathematics, Computer Engineering and Computer Science, ICMCECS 2020, Institute of Electrical and Electronics Engineers Inc., Mar. 2020. doi: 10.1109/ICMCECS47690.2020.240882.
B. Mondal, O. Patra, S. Mishra, and P. Patra, “A course recommendation system based on grades,” 2020.
Y. Zhu, H. Lu, P. Qiu, K. Shi, J. Chambua, and Z. Niu, “Heterogeneous teaching evaluation network based offline course recom-mendation with graph learning and tensor factorization,” Neurocomputing, vol. 415, pp. 84–95, Nov. 2020, doi: 10.1016/j.neucom.2020.07.064.
S. K. Thangavel, P. D. Bkaratki, and A. Sankar, “Student placement analyzer: A recommendation system using machine learning,” in 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, Jan. 2017, pp. 1–5. doi: 10.1109/ICACCS.2017.8014632.
A. J. Fernandez-Garcia, R. Rodriguez-Echeverria, J. C. Preciado, J. M. C. Manzano, and F. Sanchez-Figueroa, “Creating a Rec-ommender System to Support Higher Education Students in the Subject Enrollment Decision,” IEEE Access, vol. 8, pp. 189069–189088, 2020, doi: 10.1109/ACCESS.2020.3031572.
Y. Zayed, Y. Salman, and A. Hasasneh, “A Recommendation System for Selecting the Appropriate Undergraduate Program at Higher Education Institutions Using Graduate Student Data,” Appl. Sci., vol. 12, no. 24, Dec. 2022, doi: 10.3390/app122412525.
Q. Li and J. Kim, “A deep learning-based course recommender system for sustainable development in education,” Appl. Sci., vol. 11, no. 19, Oct. 2021, doi: 10.3390/app11198993.
J. Britto, S. Prabhu, A. Gawali, and Y. Jadhav, “A Machine Learning Based Approach for Recommending Courses at Graduate Level,” in Second International Conference on Smart Systems and Inventive Technology (ICSSIT 2019), IEEE, 2019. doi: 10.1109/ICSSIT46314.2019.8987815.
Y. Wu, “Online Course Resource Recommendation Based on Deep Learning,” in Procedia Computer Science, Elsevier B.V., 2023, pp. 638–646. doi: 10.1016/j.procs.2023.11.074.
M. Gao, Y. Luo, and X. Hu, “Online Course Recommendation Using Deep Convolutional Neural Network with Negative Sequence Mining,” Wirel. Commun. Mob. Comput., vol. 2022, 2022, doi: 10.1155/2022/9054149.
S. Amin, M. I. Uddin, W. K. Mashwani, A. A. Alarood, A. Alzahrani, and A. O. Alzahrani, “Developing a Personalized E-Learning and MOOC Recommender System in IoT-Enabled Smart Education,” IEEE Access, vol. 11, pp. 136437–136455, 2023, doi: 10.1109/ACCESS.2023.3336676.
S. Amin, M. I. Uddin, A. A. Alarood, W. K. Mashwani, A. Alzahrani, and A. O. Alzahrani, “Smart E-Learning Framework for Personalized Adaptive Learning and Sequential Path Recommendations Using Reinforcement Learning,” IEEE Access, vol. 11, pp. 89769–89790, 2023, doi: 10.1109/ACCESS.2023.3305584.
S. Amin, M. I. Uddin, A. A. Alarood, W. K. Mashwani, A. O. Alzahrani, and H. A. Alzahrani, “An adaptable and personalized framework for top-N course recommendations in online learning,” Sci. Rep., vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-56497-1.
J.-W. Tzeng, N.-F. Huang, A.-C. Chuang, T.-W. Huang, and H.-Y. Chang, “Massive open online course recommendation system based on a reinforcement learning algorithm,” Neural Comput. Appl., Jun. 2023, doi: 10.1007/s00521-023-08686-8.
R. Obeidat, R. Duwairi, and A. Al-Aiad, “A Collaborative Recommendation System for Online Courses Recommendations,” in 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), IEEE, 2019, pp. 49–54. doi: 10.1109/Deep-ML.2019.00018.
T. B. Lalitha and P. S. Sreeja, “Personalised Self-Directed Learning Recommendation System,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 583–592. doi: 10.1016/j.procs.2020.04.063.
M. Ezz and A. Elshenawy, “Adaptive recommendation system using machine learning algorithms for predicting student’s best academic program,” Educ. Inf. Technol., vol. 25, no. 4, pp. 2733–2746, Jul. 2020, doi: 10.1007/s10639-019-10049-7.
V. A. Nguyen, H.-H. Nguyen, D.-L. Nguyen, and M.-D. Le, “A course recommendation model for students based on learning out-come,” Educ. Inf. Technol., vol. 26, no. 5, pp. 5389–5415, Sep. 2021, doi: 10.1007/s10639-021-10524-0.
D. Kurniadi, E. Abdurachman, H. L. H. S. Warnars, and W. Suparta, “Predicting student performance with multi-level representa-tion in an intelligent academic recommender system using backpropagation neural network,” ICIC Express Lett. Part B Appl., vol. 12, no. 10, pp. 883–890, 2021, doi: 10.24507/icicelb.12.10.883.
N. Yanes, A. M. Mostafa, M. Ezz, and S. N. Almuayqil, “A machine learning-based recommender system for improving students learning experiences,” IEEE Access, vol. 8, pp. 201218–201235, 2020, doi: 10.1109/ACCESS.2020.3036336.
H. Ahmadian Yazdi, S. J. Seyyed Mahdavi Chabok, and M. Kheirabadi, “Dynamic Educational Recommender System Based on Improved Recurrent Neural Networks Using Attention Technique,” Appl. Artif. Intell., vol. 36, no. 1, 2022, doi: 10.1080/08839514.2021.2005298.
J. Dhar and A. K. Jodder, “An effective recommendation system to forecast the best educational program using machine learning classification algorithms,” Ing. des Syst. d’Information, vol. 25, no. 5, pp. 559–568, Nov. 2020, doi: 10.18280/ISI.250502.
G. Deepak and I. Trivedi, “A Hybridized Deep Learning Strategy for Course Recommendation,” Int. J. Adult Educ. Technol., vol. 14, no. 1, pp. 1–16, Apr. 2023, doi: 10.4018/ijaet.321752.
F. Ullah et al., “Deep Edu: A Deep Neural Collaborative Filtering for Educational Services Recommendation,” IEEE Access, vol. 8, pp. 110915–110928, 2020, doi: 10.1109/ACCESS.2020.3002544.
Q. Chen, X. Yu, N. Liu, X. Yuan, and Z. Wang, “Personalized course recommendation based on eye-tracking technology and deep learning,” in Proceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020, pp. 692–698. doi: 10.1109/DSAA49011.2020.00079.
E. L. Lee, T.-T. Kuo, and S.-D. Lin, “A Collaborative Filtering-Based Two Stage Model with Item Dependency for Course Rec-ommendation,” in 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), IEEE, Oct. 2017, pp. 496–503. doi: 10.1109/DSAA.2017.18.
D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Appl. Soft Comput., vol. 97, p. 105524, Dec. 2020, doi: 10.1016/J.ASOC.2019.105524.
C. Yu, X. Qi, H. Ma, X. He, C. Wang, and Y. Zhao, “LLR: Learning learning rates by LSTM for training neural networks,” Neuro-computing, vol. 394, pp. 41–50, 2020, doi: 10.1016/j.neucom.2020.01.106.
P. Wawrzynski, P. Zawistowski, and L. Lepak, “Automatic hyperparameter tuning in on-line learning: Classic Momentum and ADAM,” Proc. Int. Jt. Conf. Neural Networks, no. Cm, 2020, doi: 10.1109/IJCNN48605.2020.9207204.
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Copyright (c) 2025 Joseph Tersoo Iorzua, Timothy Moses, Christopher Ifeanyi Eke, Ovre Jeffery Agushaka, Dekera Kenneth Kwaghtyo, Theophilus Godswill
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