Fine-Grained Classification of Localized Educational Resources Based on Deep Knowledge Tracing Model
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Abstract
Accurate representation and classification of sequential information are critical for intelligent engineering systems involving complex signal processing and adaptive decision-making. To address the data sparsity problem in fine-grained classification of localized educational resources, this study proposes a contrast-enhanced attentional knowledge tracing framework that combines Attentive Knowledge Tracing (AKT), sliding-window sequence augmentation, and InfoNCE-based contrastive learning. The proposed method extracts cognitive state transition vectors as latent representations of resource functions and performs hierarchical clustering to identify implicit instructional categories without relying on large-scale annotated corpora or textual features. Experimental results demonstrate that the proposed framework achieves a silhouette coefficient of 0.72 under full-data conditions and maintains robust clustering performance under extremely sparse data scenarios, significantly outperforming conventional knowledge tracing models. The learned cognitive representations exhibit weak correlation with coarse-grained subject labels while effectively capturing latent functional characteristics of educational resources. By providing an efficient mechanism for high-dimensional sequential representation learning and feature discrimination, the proposed framework offers valuable insights for educational data mining, adaptive instructional design, and data-driven resource classification strategies that are relevant to intelligent tutoring systems and personalized learning applications.
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References
G. Abdelrahman, Q. Wang, and B. Nunes, “Knowledge tracing: A survey,” ACM Computing Surveys, vol. 55, no. 11, pp. 1-37, 2023, doi: 10.1145/3569576.
E. D. Bradshaw and J. K. McDonald, “Informal practices of localizing open educational resources in Ghana,” International Review of Research in Open and Distributed Learning, vol. 24, no. 2, pp. 18-36, 2023, doi: 10.19173/irrodl.v24i2.7102.
Y. Cheng, G. Wu, H. Zou, P. Luo, and Z. Cai, “A knowledge query network model based on rasch model embedding for personalized online learning,” Frontiers in Psychology, vol. 13, pp. 846621-846628, 2022, doi: 10.3389/fpsyg.2022.846621.
H. Dai, Y. Yun, Y. Zhang, R. An, W. Zhang, and X. Shang, “Self-paced contrastive learning for knowledge tracing,” Neurocomputing, vol. 609, pp. 128366-128371, 2024, doi: 10.1016/j.neucom.2024.128366.
Q. Deng, Y. Guo, Z. Yang, H. Pan, and J. Chen, “Boosting semi-supervised learning with Contrastive Complementary Labeling,” Neural Networks, vol. 170, pp. 417-426, 2024, doi: 10.1016/j.neunet.2023.11.052.
G. Feng, M. Fan, and Y. Chen, “Analysis and prediction of students’ academic performance based on educational data mining,” IEEE Access, vol. 10, pp. 19558-19571, 2022, doi: 10.1109/ACCESS.2022.3151652.
C. Guan and K. K. F. Yuen, “The cognitive comparison enhanced hierarchical clustering,” Granular Computing, vol. 7, no. 3, pp. 637-655, 2022, doi: 10.1007/s41066-021-00287-x.
H. Hu, X. Wang, Y. Zhang, Q. Chen, and Q. Guan, “A comprehensive survey on contrastive learning,” Neurocomputing, vol. 610, pp. 128645-128652, 2024, doi: 10.1016/j.neucom.2024.128645.
M. Hu, H. Chang, S. Shan, and X. Chen, “Inference calibration of Vision-Language foundation models for Zero-Shot and Few-Shot learning,” Pattern Recognition Letters, vol. 192, pp. 15-21, 2025, doi: 10.1016/j.patrec.2025.03.010.
B. Huang, Y. Lin, and C. Xu, “Contrastive label correction for noisy label learning,” Information sciences, vol. 611, pp. 173-184, 2022, doi: 10.1016/j.ins.2022.08.060.
C. Huang, W. Jiang, K. Li, J. Wu, and J. Zhang, “Enhancing learning process modeling for session-aware knowledge tracing,” Knowledge-Based Systems, vol. 309, pp. 112740-112747, 2025, doi: 10.1016/j.knosys.2024.112740.
D. Khairy, N. Alharbi, M. A. Amasha, M. F. Areed, S. Alkhalaf, and R. A. Abougalala, “Prediction of student exam performance using data mining classification algorithms,” Education and Information Technologies, vol. 29, no. 16, pp. 21621-21645, 2024, doi: 10.1007/s10639-024-12619-w.
A. Khosla, H. Singh, A. Aggarwal, and J. Bedi, “A knowledge tracing based intelligent framework for formative assessment of students skills,” Complex & Intelligent Systems, vol. 12, no. 1, pp. 22-29, 2026, doi: 10.1007/s40747-025-02149-4.
Q. Li, Q. Mai, M. Wang, and M. Ma, “Chinese dialect speech recognition: a comprehensive survey,” Artificial Intelligence Review, vol. 57, no. 2, pp. 25-33, 2024, doi: 10.1007/s10462-023-10668-0.
Q. Li, X. Yuan, J. Yue, X. Shen, R. Liang, S. Liu, and Z. Yan, “Dual-view multi-scale cognitive representation for deep knowledge tracing,” Knowledge-Based Systems, vol. 310, pp. 113010-113017, 2025, doi: 10.1016/j.knosys.2025.113010.
Y. Liu, Z. Wu, K. Deng, Z. Zheng, and C. Huang, “DisenKT: A variational attention-based approach for disentangled crossdomain knowledge tracing,” Information Processing & Management, vol. 63, no. 3, pp. 104582-104587, 2026, doi: 10.1016/j.ipm.2025.104582.
Y. Lu, L. Tong, and Y. Cheng, “Advanced Knowledge Tracing: Incorporating Process Data and Curricula Information via an Attention-Based Framework for Accuracy and Interpretability,” Journal of Educational Data Mining, vol. 16, no. 2, pp. 58-84, 2024, doi: 10.31219/osf.io/3k7z2.
F. Ma, C. Zhu, and D. Liu, “A deeper knowledge tracking model integrating cognitive theory and learning behavior,” Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6607-6617, 2024, doi: 10.3233/JIFS-235723.
Y. Pu, W. Wu, T. Peng, F. Liu, Y. Liang, X. Yu, and P. Feng, “Embedding cognitive framework with self-attention for interpretable knowledge tracing,” Scientific Reports, vol. 12, no. 1, pp. 17536-17542, 2022, doi: 10.1038/s41598-022-22539-9.
X. Ran, Y. Xi, Y. Lu, X. Wang, and Z. Lu, “Comprehensive survey on hierarchical clustering algorithms and the recent developments,” Artificial Intelligence Review, vol. 56, no. 8, pp. 8219-8264, 2023, doi: 10.1007/s10462-022-10366-3.
C. E. S. Rex, J. Annrose, and J. J. Jose, “Comparative analysis of deep convolution neural network models on small scale datasets,” Optik, vol. 271, pp. 170238-170244, 2022, doi: 10.1016/j.ijleo.2022.170238.
B. Sabiri, A. Khtira, B. El Asri, and M. Rhanoui, “Investigating contrastive pair learning’s frontiers in supervised, semi-supervised, and self-supervised learning,” Journal of Imaging, vol. 10, no. 8, pp. 196-203, 2024, doi: 10.3390/jimaging10080196.
M. Saqr, W. Matcha, N. A. A. Uzir, J. Jovanovic, D. Gaševi´c, and S. López-Pernas, “Transferring effective learning strategies across learning contexts matters: A study in problem-based learning,” Australasian Journal of Educational Technology, vol. 39, no. 3, pp. 35-57, 2023, doi: 10.14742/ajet.8303.
X. Song, J. Li, T. Cai, S. Yang, T. Yang, and C. Liu, “A survey on deep learning based knowledge tracing,” Knowledge-Based Systems, vol. 258, pp. 110036-110045, 2022, doi: 10.1016/j.knosys.2022.110036.
J. Su, X. Yu, X. Wang, Z. Wang, and G. Chao, “Enhanced transfer learning with data augmentation,” Engineering Applications of Artificial Intelligence, vol. 129, pp. 107602-107609, 2024, doi: 10.1016/j.engappai.2023.107602.
C. Tong and C. Ren, “Deep knowledge tracing and cognitive load estimation for personalized learning path generation using neural network architecture,” Scientific Reports, vol. 15, no. 1, pp. 24925-24933, 2025, doi: 10.1038/s41598-025-10497-x.
F. Ullah, I. Ullah, K. Khan, S. Khan, and F. Amin, “Advances in deep neural network-based hyperspectral image classification and feature learning with limited samples: a survey,” Applied Intelligence, vol. 55, no. 6, pp. 370-376, 2025, doi: 10.1007/s10489-024-06139-w.
Y. Wang, Y. Huo, C. Yang, X. Huang, D. Xia, and F. Feng, “Knowledge ontology enhanced model for explainable knowledge tracing,” Journal of King Saud University-Computer and Information Sciences, vol. 36, no. 5, pp. 102065-102072, 2024, doi: 10.1016/j.jksuci.2024.102065.
A. B. Wassink, C. Gansen, and I. Bartholomew, “Uneven success: automatic speech recognition and ethnicity-related dialects,” Speech Communication, vol. 140, pp. 50-70, 2022, doi: 10.1016/j.specom.2022.03.009.
Y. Xiao, R. Xiao, N. Huang, Y. Hu, H. Li, and B. Sun, “Knowledge tracing based on multi-feature fusion,” Neural Computing and Applications, vol. 35, no. 2, pp. 1819-1833, 2023, doi: 10.1007/s00521-022-07834-w.
J. Xu and W. Hu, “An enhanced deep knowledge tracing model via multi-band attention and quantized question embedding,” Applied Sciences, vol. 14, no. 8, pp. 3425-3433, 2024, doi: 10.3390/app14083425.
L. Xu, H. Xie, Z. Li, F. L. Wang, W. Wang, and Q. Li, “Contrastive learning models for sentence representations,” ACM Transactions on Intelligent Systems and Technology, vol. 14, no. 4, pp. 1-34, 2023, doi: 10.1145/3593590.
A. Zhang, L. Sheng, Z. Cai, X. Wang, and T. S. Chua, “Empowering collaborative filtering with principled adversarial contrastive loss,” Advances in Neural Information Processing Systems, vol. 36, no. 1, pp. 6242-6266, 2023, doi: 10.52202/075280-0273.
Y. Zhang, X. Cao, W. Li, and L. Zhang, “A fine-grained course session recommendation method based on knowledge point pruning,” Scientific Reports, vol. 15, no. 1, pp. 13164-13169, 2025, doi: 10.1038/s41598-025-95395-y.
W. Zhao, Z. Xu, and L. Qiu, “BPSKT: Knowledge Tracing with Bidirectional Encoder Representation Model Pre-Training and Sparse Attention,” Electronics, vol. 14, no. 3, pp. 458-465, 2025, doi: 10.3390/electronics14030458.