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A Novel Analytical Framework for Educational Intelli-gence-as-a-Service

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Abstract

Education-as-a-Service is the new proposition of this paper, which intelligence mapped with extracted knowledge from the educational data with the sole objective of modernizing the analytical process involved in it. A review of existing studies shows that majority of the scheme has focused on using direct educational data without the inclusion of practical issues in it. The proposed system introduces a novel mechanism where the traffic of unstructured educational data is subjected to unique transformation and extraction of know-ledge in presence of all practical constraints. The outcome of the study is delivered in the form of cloud services. The proposed logic is implemented in MATLAB using bigger size of educational data. The outcome accomplished shows that it facilitates better analytical operation in contrast to existing distributed software framework with better accuracy and computational process du-ration.

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References

  1. Melash, V. D., Molodychenko, V. V., Huz, V. V., Varenychenko, A. B., & Kirsanova, S. S. (2020). Modernization of education programs and formation of digital competences of future primary school teachers. International Journal of Higher Education, 9(7), 377–386.

    Article  Google Scholar 

  2. Sui, Y. (2020). The Modernization of Higher Education. In G. Fan & T. Popkewitz (Eds.), Handbook of Education Policy Studies. Singapore: Springer.

    Google Scholar 

  3. Jomah, Omer, Masoud, Amamer Khalil, Kishore, Xavier Patrick, & Aurelia, Sagaya. (2016). Micro learning: A modernized education system. BRAIN Broad research in artificial intelligence and neuroscience, 7(1), 103–110.

    Google Scholar 

  4. Hu, Q., Li, F., & Chen, C.-F. (2014). A smart home test bed for undergraduate education to bridge the curriculum gap from traditional power systems to modernized smart grids. IEEE Transactions on Education, 58(1), 32–38.

    Article  Google Scholar 

  5. Piety, Philip J., Daniel T. Hickey, and M. J. Bishop.2014. "Educational data sciences: Framing emergent practices for analytics of learning, organizations, and systems." In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, pp. 193–202.

  6. Frick, T. W. (2020). Education Systems and Technology in 1990, 2020, and Beyond. TechTrends, 64, 693–703. https://doi.org/10.1007/s11528-020-00527-y

    Article  Google Scholar 

  7. Raghupathi, V., Zhou, Y., & Raghupathi, W. (2019). Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis. International Journal of Healthcare Information Systems and Informatics (IJHISI), 14(4), 1–20.

    Article  Google Scholar 

  8. Spangenberg, Norman, Martin Roth, and Bogdan Franczyk. 2015. "Evaluating new approaches of big data analytics frameworks." In International Conference on Business Information Systems, pp. 28–37. Springer, Cham.

  9. Amini, Sasan, Ilias Gerostathopoulos, and Christian Prehofer.2017. "Big data analytics architecture for real-time traffic control." In the 2017 5th IEEE international conference on models and technologies for intelligent transportation systems (MT-ITS), pp. 710–715. IEEE.

  10. Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health information science and systems, 2(1), 3.

    Article  Google Scholar 

  11. Cardinale, Yudith, Sonia Guehis, and Marta Rukoz.2018. "Classifying Big Data Analytic Approaches: A Generic Architecture." In International Conference on Software Technologies, pp. 268–295. Springer, Cham.

  12. Govindarajan, Kannan, Thamarai Selvi Somasundaram, and Vivekanandan S. 2013. Kumar. "Continuous clustering in big data learning analytics." In 2013 IEEE Fifth International Conference on Technology for Education (t4e 2013), pp. 61–64. IEEE.

  13. Griffiths, D. (2020). The Ethical Issues of Learning Analytics in Their Historical Context. In D. Burgos (Ed.), Radical Solutions and Open Science Lecture Notes in Educational Technology. Singapore: Springer.

    Google Scholar 

  14. Arend, R. J. (2020). Strategic decision-making under ambiguity: A new problem space and a proposed optimization approach. Business Research, 13, 1231–1251. https://doi.org/10.1007/s40685-020-00129-7

    Article  Google Scholar 

  15. Ang, Kenneth Li-Minn., Ge, Feng Lu, & Seng, Kah Phooi. (2020). Big Educational Data & Analytics: Survey, Architecture and Challenges. IEEE Access, 8, 116392–1164147.

    Article  Google Scholar 

  16. Iqbal, N., Jamil, F., Ahmad, S., & Kim, D. (2020). Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries. IEEE Access, 8, 81978–81996.

    Article  Google Scholar 

  17. Lara, J. A., Sojo, A. A. D., Aljawarneh, S., Schumaker, R. P., & Al-Shargabi, B. (2020). Developing Big Data Projects in Open University Engineering Courses: Lessons Learned. IEEE Access, 8, 22988–23001.

    Article  Google Scholar 

  18. Moscoso-Zea, O., Castro, J., Paredes-Gualtor, J., & Luján-Mora, S. (2019). A Hybrid Infrastructure of Enterprise Architecture and Business Intelligence & Analytics for Knowledge Management in Education. IEEE Access, 7, 38778–38788.

    Article  Google Scholar 

  19. Yu, L., Xu, Wu., & Yang, Yu. (2019). An online education data classification model based on Tr_MAdaBoost algorithm. Chinese Journal of Electronics, 28(1), 21–28.

    Article  Google Scholar 

  20. Wang, B., Yang, Bo., Shan, S., & Chen, H. (2019). Detecting Hot Topics From Academic Big Data. IEEE Access, 7, 185916–185927.

    Article  Google Scholar 

  21. Li, X., Fan, X., Xilong, Qu., Sun, G., Yang, C., Zuo, B., & Liao, Z. (2019). Curriculum Reform in Big Data Education at Applied Technical Colleges and Universities in China. IEEE Access, 7, 125511–125521.

    Article  Google Scholar 

  22. He, H., Zheng, Q., Di, D., & Dong, Bo. (2019). How learner support services affect student engagement in online learning environments. IEEE Access, 7, 49961–49973.

    Article  Google Scholar 

  23. Yang, A.-M., Li, S.-S., Ren, C.-H., Liu, H.-X., Han, Y., & Liu, Lu. (2018). Situational awareness system in the smart campus. Ieee Access, 6, 63976–63986.

    Article  Google Scholar 

  24. Zhang, J. (2018). Spatio-Temporal Association Query Algorithm for Massive Video Surveillance Data in Smart Campus. IEEE Access, 6, 59871–59880.

    Article  Google Scholar 

  25. Xu, X., Wang, Y., & Shujiang, Yu. (2018). Teaching performance evaluation in smart campus. IEEE Access, 6, 77754–77766.

    Article  Google Scholar 

  26. Al-Rahmi, Waleed Mugahed, Yahaya, Noraffandy, Aldraiweesh, Ahmed A., Alturki, Uthman, Alamri, Mahdi M., Saud, Muhammad Sukri Bin., Kamin, Yusri Bin, Aljeraiwi, Abdulmajeed A., & Alhamed, Omar Abdulrahman. (2019). Big data adoption and knowledge management sharing: An empirical investigation on their adoption and sustainability as a purpose of education. IEEE Access, 7, 47245–47258.

    Article  Google Scholar 

  27. Kausar, S., Huahu, Xu., Hussain, I., Wenhao, Z., & Zahid, M. (2018). Integration of data mining clustering approach in the personalized E-learning system. IEEE Access, 6, 72724–72734.

    Article  Google Scholar 

  28. Qu, S., Li, K., Zhang, S., & Wang, Y. (2018). Predicting achievement of students in smart campus. IEEE Access, 6, 60264–60273.

    Article  Google Scholar 

  29. Xie, T., Zheng, Q., Zhang, W., & Huamin, Qu. (2017). Modeling and predicting the active video-viewing time in a large-scale E-learning system. IEEE Access, 5, 11490–11504.

    Article  Google Scholar 

  30. Peng, Sancheng, Guojun Wang, Yongmei Zhou, Cong Wan, Cong Wang, and Shui Yu.2017. "An immunization framework for social networks through big data based influence modeling." IEEE transactions on dependable and secure computing.

  31. Mehmood, R., Alam, F., Albogami, N. N., Katib, I., Albeshri, A., & Altowaijri, S. M. (2017). UTiLearn: A personalised ubiquitous teaching and learning system for smart societies. IEEE Access, 5, 2615–2635.

    Article  Google Scholar 

  32. Dutt, A., Maizatul, A. I., & Tutut, H. (2017). A systematic review on educational data mining. Ieee Access., 5, 15991–16005.

    Article  Google Scholar 

  33. Chou, Chih-Yueh., Tseng, Shu-Fen., Chih, Wen-Chieh., Chen, Zhi-Hong., Po-Yao Chao, K., Lai, Robert, Chan, Chien-Lung., Liang-Chih, Yu., & Lin, Yi-Lung. (2015). Open student models of core competencies at the curriculum level: Using learning analytics for student reflection. IEEE Transactions on Emerging Topics in Computing, 5(1), 32–44.

    Article  Google Scholar 

  34. Nie, Wei, Binwen Fan, Xiaomin Kong, and Qianqian Ma.2016. "Optimization of multi kernel parallel support vector machine based on Hadoop." In 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 1602–1606. IEEE.

  35. Liu, Y., Chen, X., Lixiong, Xu., Li, H., & Li, M. (2019). A resource aware parallelized back propagation neural network in enabling efficient large-scale digital health data processing. IEEE Access, 7, 114700–114713.

    Article  Google Scholar 

  36. Lnenicka, M., Kopackova, H., Machova, R., et al. (2020). Big and open linked data analytics: A study on changing roles and skills in the higher educational process. International Journal of Educational Technology in Higher Education, 17, 28. https://doi.org/10.1186/s41239-020-00208-

    Article  Google Scholar 

  37. Baig, M. I., Shuib, L., & Yadegaridehkordi, E. (2020). Big data in education: A state of the art, limitations, and future research directions. International Journal of Educational Technology in Higher Education, 17, 44. https://doi.org/10.1186/s41239-020-00223-0

    Article  Google Scholar 

  38. Jones, K. M. L. (2019). Learning analytics and higher education: A proposed model for establishing informed consent mechanisms to promote student privacy and autonomy. International Journal of Educational Technology in Higher Education, 16, 24. https://doi.org/10.1186/s41239-019-0155-0

    Article  Google Scholar 

  39. Chethan, G. S., & Vinay, S. (2019). Virtual Map-Based Approach to Optimize Storage and Perform Analytical Operation on Educational Big Data. Emerging Research in Electronics, Computer Science and Technology (pp. 401–412). Singapore: Springer.

    Chapter  Google Scholar 

  40. "Broyden–Fletcher–Goldfarb–Shanno algorithm," Wikipedia, Retrieved on 09–07–2020

  41. Phil Whelan, "How To Get Experience Working With Large Datasets," The Giant Twins, Retrieved on 09–07–2020

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Authors and Affiliations

  1. Department of Information Science and Engineering, JNNCE, Shivamogga, India

    G. S. Chethan

  2. Department of Computer Science and Engineering, PESCE, Mandya, India

    S. Vinay

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  1. G. S. Chethan
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  2. S. Vinay
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Correspondence to G. S. Chethan.

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Chethan, G.S., Vinay, S. A Novel Analytical Framework for Educational Intelli-gence-as-a-Service. Wireless Pers Commun 123, 1753–1767 (2022). https://doi.org/10.1007/s11277-021-09211-7

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