ACC
A Review of Algorithms and Challenges for Personalized Learning Path Recommendation in E-learning
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Abstract
The rapid expansion of e-learning has exposed a fundamental conflict: vast resource accessibility now clashes with rigid, one-size-fits-all instruction, failing to meet learners' personalization needs. Personalized Learning Path Recommendation (PLPR) emerges as the critical technology to resolve this conflict, a direction strongly endorsed by global education policies. This paper offers a structured review of the PLPR field. It first forges a clear classification of core algorithms into five major lineages: non-machine learning, traditional machine learning, graph-based, deep learning, and reinforcement learning. For each lineage, the analysis critically weighs documented advantages against inherent limitations, anchoring the comparison in empirical findings from representative literature to reveal their practical trade-offs. Building on this analysis, the paper dissects the field's persistent challenges—notably data sparsity, the cold-start problem, and the demands of maintaining accuracy at scale. Ultimately, this work charts a strategic path for both researchers and practitioners, aiming to connect PLPR's technical potential with the urgent, practical needs of today's digital learning environments.
Keywords
Personalized learning path recommendation; E-learning; Recommendation algorithm
References
[1] Zhong Z, Zhong SF, Tang YW. Research on construction of smart learning model supported by artificial intelligence. Res Educ Technol. 2021;42(12):71-8.
[2] Niknam M, Thulasiraman P. LPR: A bio-inspired intelligent learning path recommendation system based on meaningful learning theory. Educ Inf Technol. 2020;25(5):3797-819.
doi:10.1007/s10639-020-10133-3.
[3] de Souza GN Jr, de Deus DF, Tadaiesky V, de Araujo IM, Monteiro DC, de Santana AL. Optimizing tasks generation for children in the early stages of literacy teaching: a study using bio-inspired metaheuristics. Soft Comput. 2018;22(20):6811-24.
doi:10.1007/s00500-018-3409-1.
[4] Dwivedi P, Kant V, Bharadwaj KK. Learning path recommendation based on modified variable length genetic algorithm. Educ Inf Technol. 2018;23(2):819-36.
doi:10.1007/s10639-017-9637-7.
[5] Joseph L, Abraham S, Mani BP, Rajesh N. Exploring the effectiveness of learning path recommendation based on Felder-Silverman learning style model: a learning analytics intervention approach. J Educ Comput Res. 2022;60(6):07356331211057816.
doi:10.1177/07356331211057816.
[6] Liu H, Li X. Learning path combination recommendation based on the learning networks. Soft Comput. 2020;24(6):4427-39.
doi:10.1007/s00500-019-04205-x.
[7] Ghorbani F, Montazer GA. E-learners' personality identifying using their network behaviors. Comput Hum Behav. 2015;51(A):42-52.
doi:10.1016/j.chb.2015.04.043.
[8] Zhang X, Li M, Seng D, Chen X, Chen X. A novel precise personalized learning recommendation model regularized with trust and influence. Sci Program. 2022;2022:8479423.
doi:10.1155/2022/8479423.
[9] Nguyen CDH, Arch-Int N, Arch-Int S. Semag: a novel semantic-agent learning recommendation mechanism for enhancing learn-er-system interaction. Comput Inform. 2017;36(6):1312-34.
doi:10.4149/cai_2017_6_1312.
[10] Shi D, Wang T, Xing H, Xu H. A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning. Knowl Based Syst. 2020;195:105618.
doi:10.1016/j.knosys.2020.105618.
[11] Zhu H, et al. A multi-constraint learning path recommendation algorithm based on knowledge map. Knowl Based Syst. 2018;143:102-14.
doi:10.1016/j.knosys.2017.12.011.
[12] Zhang S, Hui N, Zhai P, Xu J, Cao L, Wang Q. A fine-grained and multi-context-aware learning path recommendation model over knowledge graphs for online learning communities. Inf Process Manag. 2023;60(5):103464.
doi:10.1016/j.ipm.2023.103464.
[13] Mu M, Yuan M. Research on a personalized learning path recommendation system based on cognitive graph with a cognitive graph. Interact Learn Environ. 2024;32(8):4237-55.
doi:10.1080/10494820.2023.2195446.
[14] Tzeng JW, Huang NF, Chen YH, Huang TW, Su YS. Personal learning material recommendation system for MOOCs based on the LSTM neural network. Educ Technol Soc. 2024;27(2):25-42.
doi:10.30191/ETS.202404_27(2).SP03.
[15] Zhang Q, Qian Y, Gao S, Liu Y, Shen X, Jiang Q. Behavioral dynamics analysis in language education: Generative state transitions and attention mechanisms. Behav Sci. 2025;15(3):326.
doi:10.3390/bs15030326.
[16] Xiong L, Chen Y, Peng Y, Ghadi YY. Improving robot-assisted virtual teaching using transformers, GANs, and computer vision. J Organ End User Comput. 2024;36(1):336481.
doi:10.4018/JOEUC.336481.
[17] Jiang B, et al. Data-driven personalized learning path planning based on cognitive diagnostic assessments in MOOCs. Appl Sci (Basel). 2022;12(8):3982.
doi:10.3390/app12083982.
[18] Liu H, Zhang T, Li F, Gu Y, Yu G. Tracking knowledge structures and proficiencies of students with learning transfer. IEEE Ac-cess. 2021;9:55413-21.
doi:10.1109/ACCESS.2020.3032141.
[19] Meng L, Zhang W, Chu Y, Zhang M. LD-LP generation of personalized learning path based on learning diagnosis. IEEE Trans Learn Technol. 2021;14(1):122-8.
doi:10.1109/TLT.2021.3058525.
[20] Melesko J, Ramanauskaite S. Time saving students' formative assessment: Algorithm to balance number of tasks and result reliabil-ity. Appl Sci (Basel). 2021;11(13):6048.
doi:10.3390/app11136048.
[21] Luo G, Gu H, Dong X, Zhou D. HA-LPR: A highly adaptive learning path recommendation. Educ Inf Technol. 2025.
doi:10.1007/s10639-025-13395-x.
[22] Han R, Chen K, Tan C. Curiosity-driven recommendation strategy for adaptive learning via deep reinforcement learning. Br J Math Stat Psychol. 2020;73(3):522-40.
doi:10.1111/bmsp.12199.
[23] Tang X, Chen Y, Li X, Liu J, Ying Z. A reinforcement learning approach to personalized learning recommendation systems. Br J Math Stat Psychol. 2019;72(1):108-35.
doi:10.1111/bmsp.12144.
[24] Su CH. Designing and developing a novel hybrid adaptive learning path recommendation system (ALPRS) for gamification math-ematics geometry course. Eurasia J Math Sci Technol Educ. 2017;13(6):2275-98.
doi:10.12973/eurasia.2017.01225a.
[25] Angeli C, Howard SK, Ma J, Yang J, Kirschner PA. Data mining in educational technology classroom research: Can it make a contribution? Comput Educ. 2017;113:226-42.
doi:10.1016/j.compedu.2017.05.021.
How to cite this paper
A Review of Algorithms and Challenges for Personalized Learning Path Recommendation in E-learning
How to cite this paper: Jing Huang, Md Gapar Md Johar. (2025) A Review of Algorithms and Challenges for Personalized Learning Path Recommendation in E-learning. Advances in Computer and Communication, 6(3), 112-119.
DOI: http://dx.doi.org/10.26855/acc.2025.07.003
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