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Performance evaluation model for operation research teaching based on IoT and Bayesian network technology

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Abstract

The rapid advancement of technology has resulted in significant changes in the field of education. In recent years, the Internet of Things (IoT) and Bayesian network technology have emerged as promising tools for enhancing operations research and teaching performance evaluation. IoT devices and sensors can provide real-time data on student performance, while Bayesian network technology can analyze and predict the complex relationships between various variables. This paper proposes a performance evaluation model based on IoT and Bayesian network technology for operations research teaching. Operations research is a broad discipline with a high level of practicality and applicability. It is widely used in business administration, production planning, traffic management, engineering construction, and financial economics. Using experiments, analysis, and quantification, operations research organizes and manages all types of resources in the system, such as human resources, funds, and goods, and formulates the best business plan. Operations research teaching must combine intuitive geometry and immaterial theorems to improve students’ understanding. The methods used in the teaching stage are simple: the teaching content could be more exciting and unintended, and it is difficult to grasp the teaching focus, making accurate evaluation of teaching performance difficult. Therefore, in the Bayesian network, the incremental results and the performance evaluation model of operations research achieve an accuracy of 87.3%. By thoroughly examining the fundamentals of Bayesian network technology, one can offer technical assistance in building an operational research teaching performance evaluation model. To combine with real-world teaching scenarios to assess the likelihood of risky events occurring during instruction and make a teaching performance evaluation model grounded in operational research. The B5 teaching effect has the highest risk probability of teaching work (0.89), and teachers with high professional titles have higher average values.

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References

  • Abbate S, Centobelli P, Cerchione R, Oropallo E, Riccio E (2022) Investigating healthcare 4.0 transition through a knowledge management perspective. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2022.3200889

    Article  Google Scholar 

  • Ali M, Yin B, Bilal H et al (2023) Advanced efficient strategy for detection of dark objects based on spiking network with multi-box detection. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-16852-2

    Article  PubMed  Google Scholar 

  • Aslam MS (2021) L 2–L∞ control for delayed singular markov switch system with nonlinear actuator faults. Int J Fuzzy Syst 23(7):2297–2308

    Article  ADS  Google Scholar 

  • Aslam MS, Qaisar I, Majid A, Shamrooz S (2023) Adaptive event-triggered robust H∞ control for Takagi-Sugeno fuzzy networked Markov jump systems with time-varying delay. Asian J Control 25(1):213–228

    Article  MathSciNet  Google Scholar 

  • Cammarano A, Varriale V, Michelino F, Caputo M (2023) Blockchain as enabling factor for implementing RFID and IoT technologies in VMI: a simulation on the Parmigiano Reggiano supply chain. Oper Manag Res 16(2):726–754

    Article  Google Scholar 

  • Charles V, Emrouznejad A, Gherman T (2023) A critical analysis of the integration of blockchain and artificial intelligence for supply chain. Ann Oper Res. https://doi.org/10.1007/s10479-023-05169-w

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  • Chen Z (2019) Observer-based dissipative output feedback control for network T-S fuzzy systems under time delays with mismatch premise. Nonlinear Dyn 95:2923–2941

    Article  Google Scholar 

  • Cheng B, Zhu D, Zhao S, Chen J (2016) Situation-aware IoT service coordination using the event-driven SOA paradigm. IEEE Trans Netw Serv Manag 13(2):349–361

    Article  Google Scholar 

  • Dai X, Hou J, Li Q, Ullah R, Ni Z, Liu Y (2020) Reliable control design for composite-driven scheme based on delay networked T-S fuzzy system. Int J Robust Nonlinear Control 30(4):1622–1642

    Article  MathSciNet  Google Scholar 

  • De Giovanni P, Belvedere V, Grando A (2022) The selection of industry 4.0 technologies through Bayesian networks: an operational perspective. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2022.3200868

    Article  Google Scholar 

  • Ding X, Wang L, Sun J, Li DY, Zheng BY, He SW, Zhu LH, Latour JM (2020) Effectiveness of empathy clinical education for children’s nursing students: a quasi-experimental study. Nurse Educ Today 85:104260

    Article  PubMed  Google Scholar 

  • Dou H, Liu Y, Chen S et al (2023) A hybrid CEEMD-GMM scheme for enhancing the detection of traffic flow on highways. Soft Comput 27:16373–16388. https://doi.org/10.1007/s00500-023-09164-y

    Article  Google Scholar 

  • Gao J, Wu D, Yin F, Kong Q, Xu L, Cui S (2023) MetaLoc: learning to learn wireless localization. IEEE J Select Areas Commun. https://doi.org/10.1109/JSAC.2023.3322766

    Article  Google Scholar 

  • Guinhouya KA (2023) Bayesian networks in project management: a scoping review. Expert Syst Appl 214:119214

    Article  Google Scholar 

  • Guo H, Li H (2022) A decomposition structure learning algorithm in Bayesian network based on a two-stage combination method. Complex and intelligent systems. Springer, Cham, pp 1–15

    Google Scholar 

  • Han X, Rani P (2022) Evaluate the barriers of blockchain technology adoption in sustainable supply chain management in the manufacturing sector using a novel Pythagorean fuzzy-CRITIC-CoCoSo approach. Oper Manag Res 15(3–4):725–742

    Article  Google Scholar 

  • Huang X, Ansari N, Huang S, Li W (2022a) Dynamic bayesian network based security analysis for physical layer key extraction. IEEE Open J Commun Soc 3:379–390

    Article  Google Scholar 

  • Huang P, Spanninger T, Corman F (2022b) Enhancing the understanding of train delays with delay evolution pattern discovery: a clustering and Bayesian network approach. IEEE Trans Intell Transp Syst 23(9):15367–15381

    Article  Google Scholar 

  • Larrañaga P, Bielza C (2023) Estimation of distribution algorithms in machine learning: a survey. IEEE Transactions on Evolutionary Computation

  • Li Q, Hou J (2021) Fault detection for asynchronous T-S fuzzy networked Markov jump systems with new event-triggered scheme. IET Control Theory Appl 15(11):1461–1473

    Article  MathSciNet  Google Scholar 

  • Li X, Sun Y (2020) Stock intelligent investment strategy based on support vector machine parameter optimization algorithm. Neural Comput Appl 32:1765–1775

    Article  Google Scholar 

  • Li QK, Lin H, Tan X, Du S (2018) H∞ consensus for multiagent-based supply chain systems under switching topology and uncertain demands. IEEE Trans Syst Man Cybern Syst 50(12):4905–4918

    Article  Google Scholar 

  • Li B, Li G, Luo J (2021) Latent but not absent: the ‘long tail’ nature of rural special education and its dynamic correction mechanism. PLoS ONE 16(3):e0242023

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li D, Ortegas KD, White M (2023) Exploring the computational effects of advanced deep neural networks on logical and activity learning for enhanced thinking skills. Systems 11(7):319

    Article  Google Scholar 

  • Liu C, Wu T, Li Z, Ma T, Huang J (2022) Robust online tensor completion for IoT streaming data recovery. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2022.3165076

    Article  PubMed  PubMed Central  Google Scholar 

  • Liu X, Wang S, Lu S, Yin Z, Li X, Yin L, Tian J, Zheng W (2023a) Adapting feature selection algorithms for the classification of Chinese texts. Systems 11(9):483

    Article  CAS  Google Scholar 

  • Liu Z, Kong X, Liu S, Yang Z (2023b) Effects of computer-based mind mapping on students’ reflection, cognitive presence, and learning outcomes in an online course. Distance Educ. https://doi.org/10.1080/01587919.2023.2226615

    Article  Google Scholar 

  • Liu Y, Li G, Lin L (2023c) Cross-modal causal relational reasoning for event-level visual question answering. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2023.3284038

    Article  PubMed  Google Scholar 

  • Liu Z, Wen C, Su Z, Liu S, Sun J, Kong W, Yang Z (2023) Emotion-semantic-aware dual contrastive learning for epistemic emotion identification of learner-generated reviews in MOOCs. IEEE Transactions on Neural Networks and Learning Systems

  • Ma K, Li Z, Liu P, Yang J, Geng Y, Yang B, Guan X (2021) Reliability-constrained throughput optimization of industrial wireless sensor networks with energy harvesting relay. IEEE Internet Things J 8(17):13343–13354

    Article  Google Scholar 

  • Nuttah MM, Roma P, Nigro GL, Perrone G (2023) Understanding blockchain applications in Industry 4.0: from information technology to manufacturing and operations management. J Ind Inform Integr 33:100456

    Google Scholar 

  • Qaisar I, Majid A, Ramaraj P (2021) Design of sliding mode controller for sensor/actuator fault with unknown input observer for satellite control system. Soft Comput 25(24):14993–15003

    Article  Google Scholar 

  • Tan WC, Sidhu MS (2022) Review of RFID and IoT integration in supply chain management. Oper Res Perspect 9:100229

    MathSciNet  Google Scholar 

  • Truong TC, Diep QB (2023) Technological spotlights of digital transformation in tertiary education. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3270340

    Article  Google Scholar 

  • Ullah R, Dai X, Sheng A (2020) Event-triggered scheme for fault detection and isolation of non-linear system with time-varying delay. IET Control Theory Appl 14(16):2429–2438

    Article  MathSciNet  Google Scholar 

  • Wu S (2023) Research on innovation and development of university instructional administration informatization in IoT and big data environment. Soft computing. Springer, pp 1–20

    Google Scholar 

  • Wu Q, Li X, Wang K et al (2023) Regional feature fusion for on-road detection of objects using camera and 3D-LiDAR in high-speed autonomous vehicles. Soft Comput 27:18195–18213. https://doi.org/10.1007/s00500-023-09278-3

    Article  Google Scholar 

  • Xiao J, Anwer N, Li W, Eynard B, Zheng C (2022) Dynamic Bayesian network-based disassembly sequencing optimization for electric vehicle battery. CIRP J Manuf Sci Technol 38:824–835

    Article  Google Scholar 

  • Xie X, Xie B, Xiong D, Hou M, Zuo J, Wei G, Chevallier J (2023) New theoretical ISM-K2 Bayesian network model for evaluating vaccination effectiveness. J Ambient Intell Humaniz Comput 14(9):12789–12805

    Article  Google Scholar 

  • Xu Y, Chen H, Wang Z, Yin J, Shen Q, Wang D, Huang F, Lai L, Zhuang T, Ge J, Hu X (2023) Multi-factor sequential re-ranking with perception-aware diversification. arXiv preprint arXiv:2305.12420

  • Xuemin Z, Ying R, Zenggang X, Haitao D, Fang X, Yuan L (2023) Resource-constrained and socially selfish-based incentive algorithm for socially aware networks. J Sign Process Syst. https://doi.org/10.1007/s11265-023-01896-2

    Article  Google Scholar 

  • Yao W, Guo Y, Wu Y, Guo J (2017) Experimental validation of fuzzy PID control of flexible joint system in presence of uncertainties. In 2017 36th Chinese Control Conference (CCC) (pp. 4192–4197). IEEE. DOI: https://doi.org/10.23919/ChiCC.2017.8028015

  • Yao Y, Yang M, Wang J, Xie M (2022) Multivariate time-series prediction in industrial processes via a deep hybrid network under data uncertainty. IEEE Trans Industr Inf 19(2):1977–1987

    Article  Google Scholar 

  • Yin B, Khan J, Wang L, Zhang J, Kumar A (2019) Real-time lane detection and tracking for advanced driver assistance systems. In 2019 Chinese Control Conference (CCC) (pp. 6772–6777). IEEE. DOI: https://doi.org/10.23919/ChiCC.2019.8866334

  • Zhang H, Mi Y, Fu Y, Liu X, Zhang Y, Wang J, Tan J (2023) Security defense decision method based on potential differential game for complex networks. Comput Secur 129:103187

    Article  Google Scholar 

  • Zhao X, Yang M, Qu Q, Xu R, Li J (2022) Exploring privileged features for relation extraction with contrastive student-teacher learning. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2022.3161584

    Article  Google Scholar 

Download references

Funding

This study was funded by the Zhejiang philosophy and social science planning project “Innovation and Practical Research on University Data Governance Model under the Background of Digital Reform” (Project ID:22NDJC108YB).

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  1. Office of Information Technology, Zhejiang University of Finance and Economics, Hangzhou, 310018, Zhejiang, China

    Linjun Kong

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Kong, L. Performance evaluation model for operation research teaching based on IoT and Bayesian network technology. Soft Comput 28, 3613–3631 (2024). https://doi.org/10.1007/s00500-024-09632-z

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