Research on the Construction of Real-Time Monitoring and Dynamic Evaluation Index System for Teaching Process Supported by Big Data Technology
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Abstract
Current educational evaluation systems often suffer from fragmented data collection, single-dimensional assessment, and delayed feedback, making it difficult to perceive learning processes in real time or provide timely intervention. This study develops a real-time monitoring and dynamic evaluation index system for teaching processes supported by big data technology. The Delphi method is used to select indicators, and the Analytic Hierarchy Process determines weights for a multidimensional model covering learning behavior, cognitive development, affective attitude, and social interaction, with 23 secondary indicators. A Hadoop distributed storage architecture, Spark real-time computing engine, and machine-learning models are used to process large-scale teaching data and identify learning states. Empirical research across three universities and 1,247 students shows that the system achieves 97.3% data collection accuracy under high-concurrency conditions, with video behavior data reaching 98.6% accuracy and text input reaching 95.1%. The system provides precise support for teaching intervention and is applicable to wireless sensing networks, smart classrooms, and electromagnetic-compatible educational monitoring environments.
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References
H. Yang and J. Wang, “An online teaching process monitoring method of MOOC platform based on video recognition,” International Journal of Continuing Engineering Education and Life-Long Learning, vol. 33, no. 2/3, pp. 149, 2023, doi: 10.1504/ijceell.2023.129203.
K. C. Lin, Y. H. Lin, and M. Y. Chen, “A Realtime Classroom Assessment System for Analysis of Students’ Evaluation of Teaching Through a Deep Learning and Emotional Contagion Mechanism,” International Journal of Interactive Multimedia & Artificial Intelligence, vol. 9, no. 5, 2025, doi: 10.9781/ijimai.2025.03.002.
B. A. D. Morais, D. R. A. M. Salha, and C. A. G. Blaha, “The importance of monitoring in remote teaching: Experiences of a nursing student in the collaborative teaching-learning process in academic training,” INTERDISCIPLINARITY AND INNOVATION IN SCIENTIFIC RESEARCH, 2023, doi: 10.56238/interdiinovationscrese-067.
U. Iroegbu, K. Drury, F. Mcgill, et al., “Quality improvement project on improving the process of antimicrobial therapeutic drug monitoring in Leeds Teaching Hospitals,” Clinical Infection in Practice, vol. 25, Art. no. 100478, 2025, doi: 10.1016/j.clinpr.2025.100478.
L. V. Shkerina, A. Gavrilyuk, O. Tabinova, et al., “Bi-subject monitoring of the results of mastering universal educational activities by students in 7th-9th grades in the process of teaching mathematics,” 2020, doi: 10.32744/pse.2020.2.15.
M. D. A. Nogueira, Márcia Cristina Monteiro dos Reis, J. C. Barbosa, et al., “Contribution of academic monitoring to the teaching-learning process: Experience report of monitors in the discipline Nursing Care in Emergency and Trauma,” International Journal of Advanced Engineering Research and Science, vol. 7, no. 7, pp. 92-96, 2020, doi: 10.22161/ijaers.77.11.
N. Lozovaya, “TECHNIQUE FOR COMPUTER MONITORING OF COMPETENCIES DEVELOPMENT AMONG UNIVERSITY STUDENTS SPECIALIZING IN ENGINEERING IN THE PROCESS OF TEACHING THEM MATHEMATICS,” 2020, doi: 10.25146/1995-0861-2020-51-1-183.
V. Gupta, V. Viswesh, C. Cone, et al., “Qualitative Analysis of the Impact of Changes to the Student Evaluation of Teaching Process,” American Journal of Pharmaceutical Education, vol. 84, no. 1, 2020, doi: 10.5688/ajpe7110.
H. Dekhn, “Evaluation of Teaching Process Using Capability Index,” 2020, doi: 10.30684/etj.37.4C.2.
G. Jianliang, “Application of SVM Algorithm in Teaching Process Evaluation,” 2021, doi: 10.1007/978-3-030-51556-0_64.