The rapid expansion of educational big data has created new opportunities for intelligent monitoring, predictive analysis, and proactive intervention in academic management systems. However, identifying abnormal student behaviors from large-scale heterogeneous educational datasets remains a challenging problem due to complex behavioral patterns, high-dimensional data structures, and evolving learning environments. This research proposes an Intelligent Isolation Forest Framework for Student Behavior Anomaly Detection in Educational Big Data Analytics, designed to detect irregular behavioral patterns through optimized unsupervised learning mechanisms. The proposed framework integrates educational data preprocessing, behavioral feature representation, isolation-based anomaly scoring, and adaptive pattern interpretation to enhance the identification of students exhibiting unusual academic or engagement behaviors. The theoretical foundation is based on anomaly detection principles and ensemble learning strategies, where isolation mechanisms are optimized for educational environments characterized by diverse behavioral attributes. Existing studies on student risk identification, ensemble algorithms, activity recognition, and iForest-based detection provide valuable foundations for developing intelligent educational analytics solutions (Gupta et al., 2022; Wu et al., 2021). The framework contributes to educational management by enabling early identification of potential learning difficulties, supporting personalized interventions, and improving decision-making efficiency. The research further analyzes the effectiveness, limitations, and practical implications of applying isolation-based intelligence to educational big data environments.

Educational Big Data Analytics, Isolation Forest, Student Behavior, Anomaly Analysis

A. Gupta, D. Garg, and P. Kumar, “An ensembling model for early identification of at-risk students in higher education,” Comput. Appl. Eng. Educ., vol. 30, no. 2, pp. 589–608, 2022.

Y. Fang, B. Luo, T. Zhao, D. He, B. B. Jiang, and Q. L. Liu, “ST-SIGMA: Spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting,” CAAI Trans. Intell. Technol., vol. 7, no. 4, pp. 744–757, 2022.

Y. Fang, B. Xie, B. Jiang, X. Ke, and Y. Li, “SPPT: Siamese pyramid pooling transformer for visual object tracking,” Hum.-Centric Comput. Inf. Sci., Dec. 30, 2023.

Y. Guo, Z. Mustafaoglu, and D. Koundal, “Spam detection using bidirectional transformers and machine learning classifier algorithms,” J. Comput. Cogn. Eng., vol. 2, no. 1, pp. 5–9, 2022.

J. Chen, J. Talley, and K. Kelly, “Infrared object classification with a hybrid optical convolution neural network,” Appl. Opt., vol. 60, no. 25, pp. G224–G231, 2021.

X. Li, “Random forest is a specific algorithm, not omnipotent for all datasets,” J. Appl. Entomol., vol. 50, no. 4, pp. 170–179, 2019.

V. S. Lalapura, J. Amudha, and H. S. Satheesh, “Recurrent neural networks for edge intelligence: A survey,” ACM Comput. Surv., vol. 54, no. 4, pp. 1–38, 2021.

S. Liaqat et al., “Novel ensemble algorithm for multiple activity recognition in elderly people exploiting ubiquitous sensing devices,” IEEE Sens. J., vol. 21, no. 16, pp. 18214–18221, 2021.

H. Y. Yatbaz, E. Ever, and A. Yazici, “Activity recognition and anomaly detection in e-Health applications using color-coded representation and lightweight CNN architectures,” IEEE Sens. J., vol. 21, no. 13, pp. 14191–14202, 2021.

T. Wu, Y. A. Zhang, and X. Tang, “Online detection of events with low-quality synchrophasor measurements based on iForest,” IEEE Trans. Ind. Inf., vol. 17, no. 1, pp. 168–178, 2021.

K. Ramamurthy, S. Gumber, W. M. Abdelfattah et al., “Human AI trust modeling in cognitive systems via ensemble learning and advanced feature engineering,” Discov. Artif. Intell., vol. 6, Art. no. 366, 2026, doi: 10.1007/s44163-026-01255-7.

P. G. Philip, “Artificial intelligence–driven intelligent project management: An integrated framework for planning, scheduling and control in engineering and construction projects,” J. Eng. Artif. Intell., vol. 02, no. 02, pp. 01–09, 2026, doi: 10.64142/jeai.2.2.50.

S. R. Lankala, M. R. Marri, A. Jain, G. G. Battu, U. Lakhina, and S. Singla, “Optimal financial fraud detection and alerting mechanism in cloud computing using deep belief network,” in Proc. 2025 Int. Conf. Emerging Trends in Networks and Computer Communications (ETNCC), Windhoek, Namibia, 2025, pp. 743–748, doi: 10.1109/ETNCC66224.2025.11299665.