Regional Network Education Information Collection Platform for Smart Classrooms based on Big Data Technology

Yuyao Li, Ashutosh Sharma

Abstract


Big data technology plays an important role in optimizing the education intelligence by enhancing the learning experience through novel assessment strategies and predictive teaching. This work aims at improving the learning efficiency of users and increase the effective learning quality of the smart classroom concept using the big data technology. A regional network education information collection platform based on big data technology has been developed, which can collect student learning data for subsequent analysis and processing. The software architecture of this platform is mainly divided into basic layer, platform layer and access layer. The physical structure mainly includes web servers and Hadoop clusters. The big data acquisition platform of this education has excellent acquisition performance, and the data acquisition of different data item field length is larger than the expected index. The outcomes obtained reveals that the data item field is less than 20 and the acquisition amount is 145% of the expected result. When the data item field is between 20 and 40, the acquisition amount is 137% of the expected result. The collection is 116% of the expected result, when the data item field is between 40 and 50. When the data item field is between 50 and 60, the collection is 103% of the expected result. The comparison of the regular teaching method with the smart classroom based concept revealed that the big data platform is 87% better than the normal regular teaching methods in terms of study material as well as meaningful teacher-student interaction. The research on education big data can provide better education services for educational activities, drive the reform of teaching mode and optimize the teaching methods of education enabling the smart classroom concept.


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Cui, L. (2020). Research on the filtering recommendation technology of network information based on big data environment. International Journal of Internet Protocol Technology, 13(4), 211-218. https://doi.org/10.1504/IJIPT.2020.110308.

Lin, R., Xie, Z., Hao, Y., & Wang, J. (2020). Improving high-tech enterprise innovation in big data environment: a combinative view of internal and external governance. International Journal of Information Management, 50, 575-585. https://doi.org/10.1016/j.ijinfomgt.2018.11.009.

Ta, N., Li, H., Liu, S., & Zuo, Y. (2020). Mining Key Regulators of Cell Reprogramming and Prediction Research Based on Deep Learning Neural Networks. IEEE Access, 8, 23179-23185. https://doi.org/10.1109/ACCESS.2020.2970442.

Liñán, L. C., & Pérez, Á. A. J. (2015). Educational Data Mining and Learning Analytics: differences, similarities, and time evolution. International Journal of Educational Technology in Higher Education, 12(3), 98-112. https://doi.org/10.1145/2460296.2460332.

Alexander, S., Barnett, D., Mann, S., Mackay, A., Selinger, M., & Whitby, G. (2013). Beyond the classroom: A new digital education for young Australians in the 21st century. Digital Education Advisory Group [DEAG], Commonwealth of Australia. Retrieved June, 7, 2015.

Sharma, A., & Kumar, R. (2017, December). An optimal routing scheme for critical healthcare HTH services—an IOT perspective. In 2017 Fourth International Conference on Image Information Processing (ICIIP), pp. 1-5. IEEE. https://doi.org/10.1109/ICIIP.2017.8313784.

Sharma, A., Tomar, R., Chilamkurti, N., & Kim, B. G. (2020). Blockchain based smart contracts for internet of medical things in e-healthcare. Electronics, 9(10), 1609. https://doi.org/10.3390/electronics9101609.

Linmei, L. I. A. N. G. (2010). Transforming American Education: Learning Powered by Technology——Interpretation and Analysis of National Educational Technology Plan 2010 in the US. Open Education Research, 4.

Bamiah, S. N., Brohi, S. N., & Rad, B. B. (2018). Big data technology in education: Advantages, implementations, and challenges. Journal of Engineering Science and Technology, 13, 229-241. https://doi.org/10.3390/socsci9040053.

Rathee, G., Sharma, A., Kumar, R., & Iqbal, R. (2019). A secure communicating things network framework for industrial IoT using blockchain technology. Ad Hoc Networks, 94, 101933. https://doi.org/10.1016/j.adhoc.2019.101933.

Rathee, G., Sharma, A., Saini, H., Kumar, R., & Iqbal, R. (2019). A hybrid framework for multimedia data processing in IoT-healthcare using blockchain technology. Multimedia Tools and Applications, 1-23. https://doi.org/10.1007/s11042-019-07835-3.

Yuvaraj, N., Srihari, K., Dhiman, G., Somasundaram, K., Sharma, A., Rajeskannan, S., ... & Masud, M. (2021). Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/6644652.

Matas Terrón, A., Leiva Olivencia, J. J., & Franco Caballero, P. D. (2020). Big Data irruption in education. Pixel-Bit: Revista de Medios y Educación, 57, 59-90. https://doi.org/10.12795/pixelbit.2020.i57.02.

Attaran, M., Stark, J., & Stotler, D. (2018). Opportunities and challenges for big data analytics in US higher education: A conceptual model for implementation. Industry and Higher Education, 32(3), 169-182. https://doi.org/10.1177/0950422218770937.

Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4), 1-34.

Azevedo, J. M., Torres, C., Lopes, A. P., & Babo, L. (2017, April). Learning Analytics: A Way to Monitoring and Improving Students' Learning. In Special Session on Analytics in Educational Environments, Vol. 2, pp. 641-648. SCITEPRESS. https://doi.org/10.5220/0006390106410648.

Daniel, B. (2015). Big Data and analytics in higher education: Opportunities and challenges. British journal of educational technology, 46(5), 904-920. https://doi.org/10.1111/bjet.12230.

Cope, B., & Kalantzis, M. (2016). Big data comes to school: Implications for learning, assessment, and research. aera Open, 2(2), 2332858416641907. https://doi.org/10.1177/2332858416641907.

Zhang, J. (2019). Research on adaptive recommendation algorithm for big data mining based on Hadoop platform. International Journal of Internet Protocol Technology, 12(4), 213-220. https://doi.org/10.1504/IJIPT.2019.103712.

Xia, D., Ning, F., & He, W. (2020). Research on parallel adaptive Canopy-K-Means clustering algorithm for big data mining based on cloud platform. Journal of Grid Computing, 18(2), 263-273. https://doi.org/10.1007/s10723-019-09504-z.

Hu, H., Fang, L., Yang, C., & Zhang, Y. (2020, December). Research on Cloud Storage Platform Technology Based on Hadoop. In Journal of Physics: Conference Series, Vol. 1693, No. 1, p. 012015. IOP Publishing. https://doi.org/10.1109/ICAwST.2011.6163114.

Songsangyos, P., & Nilsook, P. (2015). Big Data in the Cloud for Education Institutions. In The Twelfth International Conference on eLearning for Knowledge-Based Society. https://doi.org/10.1145/2538862.2538949.

Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1), 1-16. https://doi.org/10.1186/s40537-019-0206-3.

Altaye, A. A., & Nixon, J. S. (2019). A Comparative Study on Big Data Applications in Higher Education. International Journal of Emerging Trends in Engineering Research, 7(12), 739-745. https://doi.org/10.30534/ijeter/2019/027122019.

Kumar, S., & Singh, M. (2018). Big data analytics for healthcare industry: impact, applications, and tools. Big Data Mining and Analytics, 2(1), 48-57. https://doi.org/10.26599/BDMA.2018.9020031.

Rizk, R., McKeever, S., Petrini, J., & Zeitler, E. (2019). Diftong: a tool for validating big data workflows. Journal of Big Data, 6(1), 1-27. https://doi.org/10.1186/s40537-019-0204-5.

Al-Barashdi, H., & Al-Karousi, R. (2019). Big Data in academic libraries: literature review and future research directions. Journal of Information Studies & Technology (JIS&T), 2018(2), 13. https://doi.org/10.5339/jist.2018.13.

Cravero, A. (2018). Big data architectures and the internet of things: A systematic mapping study. IEEE Latin America Transactions, 16(4), 1219-1226. https://doi.org/10.1109/TLA.2018.8362160.

Li, L., Lu, Z., Chen, Z., Cui, Y., Kuang, Y., & Wang, F. (2019). Parallel computation of regional CORS network corrections based on ionospheric-free PPP. GPS Solutions, 23(3), 1-12. https://doi.org/10.1007/s10291-019-0864-9.

Bordel, B., Orúe, A. B., Alcarria, R., & Sánchez-De-Rivera, D. (2018). An intra-slice security solution for emerging 5G networks based on pseudo-random number generators. IEEE Access, 6, 16149-16164. https://doi.org/10.1109/ACCESS.2018.2815567.

Niculescu, M. F., Wu, D. J., & Xu, L. (2018). Strategic intellectual property sharing: Competition on an open technology platform under network effects. Information Systems Research, 29(2), 498-519. https://doi.org/10.1287/isre.2017.0756.

Jiao, H., Wei, X., Liang, D., & Yang, W. (2019). Research on Optimization Model of Empty Car Distribution among Regional Railways Based on Freight Service Network. Advances in Applied Mathematics, 8(11), 1816-1826. https://doi.org/10.12677/AAM.2019.811212.

Lu, W., Liang, L., Wu, X., Wang, X., & Cai, J. (2020). An adaptive multiscale fusion network based on regional attention for remote sensing images. IEEE Access, 8, 107802-107813. https://doi.org/10.1109/ACCESS.2020.3000425.




DOI: https://doi.org/10.31449/inf.v45i5.3555

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