Next-Gen Education with 6G Connectivity and Intelligent Data Analytics
The aims to assess the efficacy of data extraction in educational software and determine the function of 6G IoT connectivity in improving educational systems. An Intelligent Data Analytics Model (IDAM) is suggested in this article to revolutionise evaluations of educational quality. Initial steps in IDAM's theoretical groundwork in education data mining and the Internet of Things (IoT) include collecting, organizing, and analyzing data from higher education and graduate outcomes. One goal is to use survey data and quantitative analysis to answer the problems that arise when comparing the quality of education at different schools. By utilizing insights from data-mining techniques utilized at the University of Science and Technology, the model intends to create an improved framework for educational quality assessment. The efficacy of this model will be evaluated in subsequent analyses. By linking IoT sensors and interacting with protocols to record learning behaviours, the results show that 6G IoT gear can improve educational monitoring. Passing information from the layer of perception to the protocol stack enables this system to provide real-time insights regarding instructional assets and educational quality. Overall, educational outcomes are improved in a connected, data-driven environment when the Internet of Things (IoT) is integrated with data-gathering tools. This allows for continuous assessment of educator performance.
Authors
- Jamal K. Abbas (ORCID: https://orcid.org/0000-0002-5102-9538)
- Ahmed Hussain (ORCID: https://orcid.org/0009-0008-4037-2358)
- Abdul Mohsen
- Farooq Hussain (ORCID: https://orcid.org/0000-0001-7637-3741)
- Sohaib Hassan (ORCID: https://orcid.org/0000-0002-5027-4320)
Institutions
- University of Kirkuk (IQ)
- Al-Nisour University College (IQ)
Publication Details
- Published
- 2024-12-12
- DOI
- https://doi.org/10.1109/icercs63125.2024.10895621
- Citations
- 3
- Primary Topic
- Telecommunications and Broadcasting Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.96