Information and Communications Technology Intelligence Risk Assessment for Digital Universities

Authors

  • Denchai Panket Bansomdejchaopraya Rajabhat University, Thailand
  • Montree Chinsomboon Valaya Alongkorn Rajabhat University under The Royal Patronage, Thailand
  • Kiatikhorn Sobhanabhorn Bansomdejchaopraya Rajabhat University, Thailand

Keywords:

Risk assessment, Data Intelligent, Digital University

Abstract

This document Currently, the technological advance has an important part in developing the university in entering to the digital organization for helping to develop the organization to have the progress and will help to increase the efficiency in the management in every side rapidly via the technology which will have the accuracy and the precision. However, this will have the risk which may give the effect to let the organization create the damage. This will be needed to have to evaluate the technological risk for the organization to plan to cope with the effect that may be happening in the future. The risk management by the intelligent information technology, this is found that from evaluating of the risk with the intelligent information in the information technology for the digital university, this has components which have risk factors that are happened in the university which have both internal factors, external factors, the analysis of the data intelligent for evaluating the risk in the digital university. This has the information and the technology that are important parts in driving for evaluating other risk levels that are happened within the organization from the large database with the  in-depth analysis for examining the error and showing the guideline for making the decision in order to create the least effect on the personnel, the student, the stakeholder and the community for bringing to the creation of the digital organization for the education effectively and having the safety sustainably.

References

Abaoud, D. (2020). The relationship between knowledge management practices and enterprise risk management in

higher education: An action research. 27th European Conference on Information Systems – Information

Systems for a Sharing Society, ECIS 2019, 0–15.

Anyim, W. O. (2020). Internal Control and Risk Management System in University Libraries:

Applications, Techniques and Limitations. Library Philosophy and Practice, 2020(October), 1–21.

Ateş, V., & Güneş, B. (2018). The factors affecting information technologies risk management at

Turkey’s state universities. International Journal of EBusiness and EGovernment Studies, 10(2), 46–62.

Beecher, B., & Streitwieser, B. (2019). A Risk Management Approach for theInternationalization

of Higher Education. Journal of the Knowledge Economy, 10(4), 1404–1426. https://doi.org/10.1007/s13132-017-0468-y

Berdykulova, G., Ipalakova, M., Kamysbayev, M., & Daineko, Y. (2020). Towards digital

university: Experience of kazakhstan. ACM International Conference Proceeding Series. https://doi.org/10.1145/3410352.3410793

Bilyalova, A., Salimova, D., & Zelenina, T. (2020). Higher Education in Digital Age. Advances

in Intelligent Systems and Computing, 1114 AISC, 207–219. https://doi.org/10.1007/978-

-030-37737-3_19

Channgam, S., Nilsook, P., & Wannapiroon, P. (2019). Intelligent information management with

digitization workflow. International Journal of Machine Learning and Computing, 9(6),

–892. https://doi.org/10.18178/ijmlc.2019.9.6.888

Dash Wu, D. (2020). Data intelligence and risk analytics. Industrial Management and Data

Systems, 120(2), 249–252. https://doi.org/10.1108/IMDS-02-2020-606

Dehdashti, A., Fatemi, F., Janati, M., Asadi, F., & Kangarloo, M. B. (2020). Data of risk analysis

management in university campuses. BMC Research Notes, 13(1), https://doi.org/10.1186/s13104-020-05397-4

Denzler, A., & Kaufmann, M. (2017). Toward granular knowledge analytics for data intelligence:

Extracting granular entity-relationship graphs for knowledge profiling. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, 2018-Janua, 923–928. https://doi.org/10.1109/BigData.2017.8258010

Khalil, N., Kamaruzzaman, S. N., & Baharum, M. R. (2016). Analytical Hierarchy Process for

Developing a Building Performance-Risk Rating Tool. MATEC Web of Conferences, 66. https://doi.org/10.1051/matecconf/20166600123

Kim, D., Yun, J. J., & Hagen, L. (2020). DG . O 2020 : Governance in the Era of Data

Intelligence. 524–526.

Kozlova, A., & Snegurenko, A. (2019). University risk assessment and management system. IOP

Conference Series: Materials Science and Engineering, 666(1). https://doi.org/10.1088/1757-899X/666/1/012050

Lezer, V. A., Shabatura, L. N., & Karnaukhov, I. A. (2020). The Flagship University’s Model in

Terms of Digitalization: The Case of Industrial University of Tyumen as a Center of Strategic Decisions in the Field of Smart-City, IoT/IIoT and Big Data. Lecture Notes in Networks and Systems, 91, 387–396. https://doi.org/10.1007/978-3-030-32015-7_43

McDonald, T. J., O’Byrne, D., O’Leary, P., & O’Riordan, C. (2020). Development of an

academic risk model to support higher education quality assurance. International Conference on Higher Education Advances, 2020-June, 1323–1329. https://doi.org/10.4995/HEAd20.2020.11261

Orozova, D., Kaloyanova, K., & Todorova, M. (2019). Introducing information security concepts

and standards in higher education. TEM Journal, 8(3), 1017–1024. https://doi.org/10.18421/TEM83-46

Perez Gama, J. A., Vega Vega, A., & Neira Aponte, M. (2018). University digital transformation

intelligent architecture: A dual model, methods and applications. Proceedings of the LACCEI International Multi-Conference for Engineering, Education and Technology, 2018-July(July), 19–21. https://doi.org/10.18687/LACCEI2018.1.1.274

Popova, T. N., Mitrofanova, Y. S., Ivanova, O. A., & Vereshchak, S. B. (2020). Economic and

Organizational Aspects of University Digital Transformation. Smart Innovation, Systems and Technologies, 188, 371–381. https://doi.org/10.1007/978-981-15-5584-8_32

Rozhkova, D., Rozhkova, N., & Blinova, U. (2020). Digital Universities in Russia: Prospects and

Problems. Advances in Intelligent Systems and Computing, 1114 AISC, 252–262. https://doi.org/10.1007/978-3-030-37737-3_23

Safhi, H. M., Frikh, B., Hirchoua, B., Ouhbi, B., & Khalil, I. (2017). Data intelligence in the

context of big data: A survey. Journal of Mobile Multimedia, 13(1–2), 1–27.

Secundo, G., Rippa, P., & Cerchione, R. (2020). Digital Academic Entrepreneurship: A

structured literature review and avenue for a research agenda. Technological Forecasting and Social Change, 157(May), 120118. https://doi.org/10.1016/j.techfore.2020.120118

Shikhnabieva, T. (2020). Knowledge-Based Model Representation for a Modern Digital

University. Smart Innovation, Systems and Technologies, 188, 55–65. https://doi.org/10.1007/978-981-15-5584-8_5

Singh, U. K., & Joshi, C. (2017). Information security risk management framework for

University computing environment. International Journal of Network Security, 19(5), 742–751. https://doi.org/10.6633/IJNS.201709.19(5).12

Suroso, J. S., & Fakhrozi, M. A. (2018). Assessment of Information System Risk Management

with Octave Allegro at Education Institution. Procedia Computer Science, 135, 202–213. https://doi.org/10.1016/j.procs.2018.08.167

Zhang, X., & Yang, D. (2019). The development of image creation method by data intelligence-

based automated pastiche. Proceedings of 2019 IEEE 2nd International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2019, 129–135. https://doi.org/10.1109/AUTEEE48671.2019.9033430

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Published

2022-06-30

How to Cite

[1]
D. Panket, M. Chinsomboon, and K. Sobhanabhorn, “Information and Communications Technology Intelligence Risk Assessment for Digital Universities”, Int J Edu Comm Tech, vol. 2, no. 1, pp. 34–46, Jun. 2022.

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Original Articles