Author(s)

Qiang Li ¹, Xinyu Zhai ¹, Jin Wang ¹

Affiliation(s)

¹ Vocational and Technical College, Hebei Normal University, Shijiazhuang, Hebei, China

Corresponding Author

Jin Wang

ABSTRACT

To address the personalized and diverse needs of students during instruction, this paper integrates the concept of fit into reinforcement learning algorithms to meet multi-level learning demands and develops an adaptive learning path planning platform. First, the concept of "fit" is introduced, focusing on analyzing the relationship between learners and learning content. This relationship is then represented through data across three dimensions: educational, technological, and subject-related. Second, an adaptive learning path generation mechanism based on reinforcement learning was designed. This mechanism can automatically generate learning paths according to learner characteristics and existing learning resources and adaptively adjust them based on individual learner needs. Finally, an adaptive learning path platform was designed using reinforcement learning algorithms and the concept of fit. Experiments confirmed that the designed platform can apply adaptive learning path planning mechanisms to real personalized teaching needs, demonstrating the feasibility and effectiveness of the adaptive algorithmic mechanism.

KEYWORDS

Personalized Needs, Degree of Fit, Reinforcement Learning, Path Planning

CITE THIS PAPER

Qiang Li, Xinyu Zhai, Jin Wang. Design and Implementation of an Adaptive Learning Path Planning Platform for Multi-Level Instructional Needs. Curriculum and Teaching Methodology (2026). Vol. 9, No.2, 105-112. DOI: http://dx.doi.org/10.23977/curtm.2026.090213.

REFERENCES

[1] Alrawashdeh G S, Fyffe S, Azevedo R F L, et al. Exploring the impact of personalized and adaptive learning technologies on reading literacy: A global meta-analysis[J]. Educational Research Review, 2024, 42: 100587.
[2] Du Plooy E, Casteleijn D, Franzsen D. Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement[J]. Heliyon, 2024, 10(21).
[3] Mustafa M Y, Tlili A, Lampropoulos G, et al. A systematic review of literature reviews on artificial intelligence in education (AIED): A roadmap to a future research agenda[J]. Smart Learning Environments, 2024, 11(1): 59.
[4] Huang C, Zhong Y, Li Y, et al. Enhancing student reading performance through a personalized two-tier problem-based learning approach with generative artificial intelligence[J]. Humanities and Social Sciences Communications, 2025, 12(1): 1-16.
[5] Pelánek R .Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling[J].International Journal of Artificial Intelligence in Education,2024,35(1):1-26.DOI:10.1007/S40593-024-00400-6.
[6] ESSALMI F, AYED L J B, JEMNI M, et al. Generalized metrics for the analysis of e-learning personalization strategies[J]. Computers in Human Behavior, 2015, 48: 310-322.
[7] CHRYSAFIADI K, VIRVOU M. Student modeling approaches:a literature review for the last decade[J]. Expert Systems with Applications, 2013, 40(11): 4715-4729.
[8] PUKKHEM N. LORecommendNet: an ontology-based representation of learning object recommendation[C]//Proceedings of the 10th International Conference on Computing and Information Technology, Angsana Laguna, May 8-9, 2014.Cham: Springer, 2014: 293-303.
[9] SHI D Q, WANG T, XING H, et al. A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning[J]. Knowledge-Based Systems, 2020, 195: 105618.
[10] SHU J B, SHEN X X, LIU H, et al. A content-based recommendation algorithm for learning resources[J]. Multimedia Systems, 2018, 24(2): 163-173.
[11] CHEN Y, TSENG C, HUANG C, et al. Recommendation system based on rule- space model of two- phase blue- red tree and optimized learning path with multimedia learning and cognitive assessment evaluation[J]. Multimedia Tools and Applications, 2017, 76(18): 18237-18264.
[12] LI X, LI X, TANG J T, et al. Improving deep item- based collaborative filtering with Bayesian personalized ranking for MOOC course recommendation[C]//LNCS 12274: Proceedings of the 13th International Conference on Knowledge Science, Engineering and Management, Hangzhou ,Aug 28-30, 2020. Cham: Springer, 2020: 247-258.

Sponsors, Associates, and Links

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• Advances in Educational Technology and Psychology
  
  
• Transactions on Comparative Education
  
  
• Applied & Educational Psychology
  
  
• Advances in Vocational and Technical Education
  
  
• Adult and Higher Education