Jing Huang1,2, Md Gapar Md Johar3,*
1School of Modern Information Industry, Guangzhou College of Commerce, Guangzhou 511363, Guangdong, China.
2School of Graduate Studies, Postgraduate Centre, Management and Science University, Shah Alam, Selangor 40100, Malaysia.
3Software Engineering and Digital Innovation Center, Management and Science University, Shah Alam, Selangor 40100, Malaysia.
*Corresponding author: Md Gapar Md Johar
This work was supported by the 2022 Guangdong Province Demonstration Project for Curriculum-integrated Civics and Virtues Education (No. 2022SJKCSZSFTD001), the 2022 Teaching Quality Project of Guangzhou College of Commerce (No. 2022ZLGC07), and the ''AI+'' Pilot Course Program (Data Structures) of Guangzhou College of Commerce (No. 2024rgznsdkc14).
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.