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Improving Performance of Smart Education Systems by Integrating Machine Learning on Edge Devices and Cloud in Educational Institutions

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Abstract

Educational institutions today are embracing technology to enhance education quality through intelligent systems. This study introduces an innovative strategy to boost the performance of such procedures by seamlessly integrating machine learning on edge devices and cloud infrastructure. The proposed framework harnesses the capabilities of a Hybrid 1D Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) architecture, offering profound insights into intelligent education. Operating at the crossroads of localised and centralised analyses, the Hybrid 1D CNN-LSTM architecture signifies a significant advancement. It directly engages edge devices used by students and educators, laying the groundwork for personalised learning experiences. This architecture adeptly captures the intricacies of various modalities, including text, images, and videos, by harmonising 1D CNN layers and LSTM modules. This approach facilitates the extraction of tailored features from each modality and the exploration of temporal intricacies. Consequently, the architecture provides a holistic comprehension of student engagement and comprehension dynamics, unveiling individual learning preferences. Moreover, the framework seamlessly integrates data from edge devices into the cloud infrastructure, allowing insights from both domains to merge. Educators benefit from attention-enhanced feature maps that encapsulate personalised insights, empowering them to customise content and strategies according to student learning preferences. The approach bridges real-time, localised analysis with comprehensive cloud-mediated insights, paving the path for transformative educational experiences. Empirical validation reinforces the effectiveness of the Hybrid 1D CNN-LSTM architecture, cementing its potential to revolutionise intelligent education within academic institutions. This fusion of machine learning across edge devices and cloud architecture can reshape the educational landscape, ushering in a more innovative and more responsive learning environment that caters to the diverse needs of students and educators alike.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Haleem, A., Javaid, M., Qadri, M.A., Suman, R.: Understanding the role of digital technologies in education: a review. Sustain. Oper. Comput. 3, 275–285 (2022)

    Article  Google Scholar 

  2. Badshah, A., Ghani, A., Daud, A., Jalal, A., Bilal, M., Crowcroft, J: Towards Smart Education through the Internet of Things: A Review. arXiv preprint arXiv:2304.12851 (2023)

  3. Xiong, Z., Liu, Q., Huang, X.: The influence of digital educational games on preschool children’s creative thinking. Comput. Educ. 189, 104578 (2022)

    Article  Google Scholar 

  4. Kaur, A., Bhatia, M.: Smart education: a systematic survey and future research directions. In: 2021 International Conference on Computing Sciences (ICCS), pp. 173–177. IEEE (2021)

  5. Guo, F., Zhou, W., Lu, Q., Zhang, C.: Path extension similarity link prediction method based on matrix algebra in directed networks. Comput. Commun. 187, 83–92 (2022)

    Article  Google Scholar 

  6. Huang, C., Han, Z., Li, M., Wang, X., Zhao, W.: Sentiment evolution with interaction levels in blended learning environments: using learning analytics and epistemic network analysis. Australas. J. Educ. Technol. 37(2), 81–95 (2021)

    Article  Google Scholar 

  7. Xu, J., Pan, S., Sun, P.Z.H., Park, S.H., Guo, K.: Human-factors-in-driving-loop: driver identification and verification via a deep learning approach using psychological behavioral data. IEEE Trans Intell Transport Syst 24, 3383–3394 (2022)

    Article  Google Scholar 

  8. Liu, X., Zhou, G., Kong, M., Yin, Z., Li, X., Yin, L. … Zheng, W.: Developing multi-labelled corpus of twitter short texts: a semi-automatic method. Systems 11(8), 390 (2023)

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

    Article  Google Scholar 

  10. Li, T., Fan, Y., Li, Y., Tarkoma, S., Hui, P.: Understanding the long-term evolution of mobile app usage. IEEE Trans. Mob. Comput. 22(2), 1213–1230 (2023)

    Article  Google Scholar 

  11. Palanivel, K.: Smart education architecture using the internet of things (IoT) technology. Int. J. Manag. IT Eng. (IJMRA) 9(4), 2 (2019)

    Google Scholar 

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

  13. Huang, F., Wang, Z., Huang, X., Qian, Y., Li, Z. … Chen, H.: Aligning Distillation For Cold-Start Item Recommendation. Paper presented at the SIGIR '23, New York, NY (2023)

  14. Kassab, M., DeFranco, J., Laplante, P.: A systematic literature review on internet of things in education: benefits and challenges. J. Comput. Assist. Learn. 36(2), 115–127 (2020)

    Article  Google Scholar 

  15. Xu, A., Qiu, K., Zhu, Y.: The measurements and decomposition of innovation inequality: based on Industry − University − Research perspective. J. Bus. Res. 157, 113556 (2023)

    Article  Google Scholar 

  16. Senthil, T., Deepika, J., Rajan, C.: Internet of Things (IoT) in Education. Harnessing the Internet of Things (IoT) for a Hyper-Connected Smart World, pp. 25 (2022)

  17. Damola, O., Adebayo, T.E., Olayinka, O.: Examining the potential of IoT-based cloud-integrated smart classroom for efficient learning and institutional sustainability. Int. Res. J. Modern. Eng. Technol. Sci. 5(5), 2436–2444

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

    Article  Google Scholar 

  19. Yang, S., Li, Q., Li, W., Li, X., Liu, A.: Dual-level representation enhancement on characteristic and context for image-text retrieval. IEEE Trans. Circuits Syst. Video Technol. 32(11), 8037–8050 (2022)

    Article  Google Scholar 

  20. Embarak, O.H.: Internet of Behaviour (IoB)-based AI models for personalized smart education systems. Procedia Comput. Sci. 203, 103–110 (2022)

    Article  Google Scholar 

  21. Zhou, X., Zhang, L.: SA-FPN: an effective feature pyramid network for crowded human detection. Appl. Intell. 52(11), 12556–12568 (2022)

    Article  Google Scholar 

  22. Omonayajo, B., Al-Turjman, F., Cavus, N.: Interactive and innovative technologies for smart education. Comput. Sci. Inf. Syst. 19(3), 1549–1564 (2022)

    Article  Google Scholar 

  23. Peng, Z., Hu, J., Shi, K., Luo, R., Huang, R., Ghosh, B.K. … Huang, J.: A novel optimal bipartite consensus control scheme for unknown multi-agent systems via model-free reinforcement learning. Appl. Math. Comput. 369, 124821 (2020)

  24. Liu, Y., Li, G., Lin, L.: Cross-modal causal relational reasoning for event-level visual question answering. IEEE Trans. Pattern Anal. Mach. Intell. 45(10), 11624–11641 (2023)

    PubMed  Google Scholar 

  25. Huang, C., Tu, Y., Han, Z., Jiang, F., Wu, F. … Jiang, Y.: Examining the relationship between peer feedback classified by deep learning and online learning burnout. Comput. Educ. 207, 104910 (2023)

  26. Ding, X., Wang, L., Sun, J., Li, D., Zheng, B., He, S. … Latour, J.M.: Effectiveness of empathy clinical education for children's nursing students: a quasi-experimental study. Nurse Educ. Today 85, 104260 (2020)

  27. Liu, C., Wu, T., Li, Z., Ma, T., Huang, J.: Robust online tensor completion for IoT streaming data recovery. IEEE Trans. Neural Netw. Learn. Syst. 34(12), 10178–10192 (2023)

    Article  MathSciNet  PubMed  Google Scholar 

  28. Chen, G., Chen, P., Wang, Y., Zhu, N.: Research on the development of an effective mechanism of using public online education resource platform: TOE model combined with FS-QCA. Interact. Learn. Environ. (2023)

  29. Zhao, X., Yang, M., Qu, Q., Xu, R., Li, J.: Exploring privileged features for relation extraction with contrastive student-teacher learning. IEEE Trans. Knowl. Data Eng. 35(8), 7953–7965 (2023)

    Google Scholar 

  30. Liu, X., Shi, T., Zhou, G., Liu, M., Yin, Z., Yin, L. … Zheng, W.: Emotion classification for short texts: an improved multi-label method. Humanit. Soc. Sci. Commun. 10(1), 306 (2023).

  31. Liu, H., Jiang, K., Gamboa, H., Xue, T., Schultz, T.: Bell shape embodying Zhongyong: the pitch histogram of traditional Chinese anhemitonic pentatonic folk songs. Appl. Sci. 12(16), 8343 (2022)

    Article  CAS  Google Scholar 

  32. Liu, Z., Kong, X., Liu, S., Yang, Z.: Effects of computer-based mind mapping on students’ reflection, cognitive presence, and learning outcomes in an online course. Distance Educ. 44(3), 544–562 (2023)

    Article  Google Scholar 

  33. Lu, S., Liu, M., Yin, L., Yin, Z., Liu, X., Zheng, W. … Kong, X.: The multi-modal fusion in visual question answering: a review of attention mechanisms. PeerJ Comput. Sci. 9, e1400 (2023)

  34. Jiang, H., Dai, X., Xiao, Z., Iyengar, A.K.: Joint task offloading and resource allocation for energy-constrained mobile edge computing. IEEE Trans. Mob. Comput. (2022)

  35. Hu, J., Wu, Y., Li, T., Ghosh, B.K.: Consensus control of general linear multiagent systems with antagonistic interactions and communication noises. IEEE Trans. Autom. Control 64(5), 2122–2127 (2019)

    Article  MathSciNet  Google Scholar 

  36. Liu, Z., Wen, C., Su, Z., Liu, S., Sun, J., Kong, W. … Yang, Z.: Emotion-semantic-aware dual contrastive learning for epistemic emotion identification of learner-generated reviews in MOOCs. IEEE Trans. Neural Netw. Learn. Syst. (2023)

  37. Medina-Quero, J., Zhang, S., Nugent, C., Espinilla, M.: Ensemble classifier of long short-term memory with fuzzy temporal windows on binary sensors for activity recognition. Expert Syst. Appl. 114, 441–453 (2018)

    Article  Google Scholar 

  38. Cao, B., Zhang, J., Liu, X., Sun, Z., Cao, W., Nowak, R.M. ... Lv, Z.: Edge–cloud resource scheduling in space–air–ground-integrated networks for internet of vehicles. IEEE Internet Things J. 9(8), 5765–5772 (2022)

  39. Quero, J.M., Orr, C., Zang, S., Nugent, C., Salguero, A., Espinilla, M.: Real-time recognition of interleaved activities based on ensemble classifier of long short-term memory with fuzzy temporal windows. In: Proceedings, vol. 2, no. 19, pp. 1225. MDPI (2018)

  40. Gao, J., Wu, D., Yin, F., Kong, Q., Xu, L. … Cui, S.: MetaLoc: learning to learn wireless localization. IEEE J. Sel. Areas Commun. (2023)

  41. Hammerla, N.Y., Halloran, S., Plötz, T.: Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv preprint arXiv:1604.08880 (2016)

  42. Alan, H.: A systematic bibliometric analysis on the current digital human resources management studies and directions for future research. J. Chin. Hum. Resour. Manag. 14(1), 38–59 (2023)

    Article  Google Scholar 

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Funding

This paper is sponsored by the Design and Application of Assistant Tool for Self-proposed examination paper Management based on Access & Grid +  + (Project No.: 2020JG06); Construction of intelligent employment service platform for college students from the perspective of Internet plus (Project No.: 220600383065541).

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Authors and Affiliations

  1. Enrollment and Employment Department, Chengdu Normal University, Chengdu, 611130, Sichuan, China

    Shujie Qiu

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  1. Shujie Qiu
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Contributions

Shujie Qiu: Conceptualization, Methodology, Formal analysis, Supervision, Investigation, Data Curation,Writing—original draft, Writing—review & editing.

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Correspondence to Shujie Qiu.

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Qiu, S. Improving Performance of Smart Education Systems by Integrating Machine Learning on Edge Devices and Cloud in Educational Institutions. J Grid Computing 22, 41 (2024). https://doi.org/10.1007/s10723-024-09755-5

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