Research Article
BibTex RIS Cite

RISING VALUE OF DATA IN CONTEMPORARY HIGHER EDUCATION

Year 2024, Volume: 20 Issue: 1, 41 - 46, 31.12.2024

Abstract

Purpose - The purpose of this study is to reflect the importance of effective use of data to predict and improve academic success as an essential criterion for assessing the quality of higher education institutions in the 21st Century. This paper intends to clarify importance of data and its evaluation components, namely Educational Data Mining (EDM), Learning Analytics (LA), Artificial intelligence (AI) and Machine Learning (ML), as integral part of Fifth Generation Universities (UNIVERSITY 5.0) era in the globalized competitive higher education sector. For this reason, this paper advocates “Rising Value of Data in Contemporary Higher Education” for the university of the new age.
Methodology - The study employs a literature review aiming to reflect the new atmosphere and requirements in the higher education system based on selected topics. A comprehensive analysis on the game changer role of data in the higher education institutions was considered. The aim was to identify the difference created by effective use of data in higher education institutions to predict and improve academic success in the competitive academic environment of the new era.
Findings - The analysis reveals that higher education institutions should understand the essential role of educational data with the expansion of digital revolution and rapid change in technologies in the 21st Century and design their strategies accordingly. Notably, it is clearly seen that the universities have not only effectively use educational data and its evaluation components namely Educational Data Mining (EDM), Learning Analytics (LA), Artificial intelligence (AI) and Machine Learning (ML) but also internalize the reality of their rising value to predict and improve academic success as well as creating a significant financial contribution to their development. As a matter of the fact, universities established many projects and effectively used their Learning Analytics (LA) tools. Besides, the emergence of Artificial intelligence (AI) and Machine Learning (ML) enhanced the efficiency and effectiveness of management operations.
Conclusion - Findings may be concluded that universities need to apply the effective use of data particularly in the context of new era like Industry 5.0, Society 5.0 and University 5.0 to obtain academic success, which is considered as an essential criterion for assessing the quality of higher education institutions. Indeed, universities have to follow a data- driven culture as greater demands of universities already appeared for retention, completion and graduation rates of students to improve student success. As a matter of fact, the effective use of Educational Data Mining (EDM) and Learning Analytics (LA) is going on for the last two decades in higher education institutions. Indeed, Artificial intelligence (AI) and Machine Learning (ML) are effective in data management as two impressive game changers for universities changing educational world from the financial perspective. For this reason, it may be argued that the effective use of data and its evaluation components, namely Educational Data Mining (EDM), Learning Analytics (LA), Artificial intelligence (AI) and Machine Learning (ML) are considered as the integral part of Fifth Generation Universities (UNIVERSITY 5.0) era in the globalized competitive higher education sector of 21st Century.

References

  • Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1), 3-18.
  • Amare, M. Y., & Šimonová, S. (2021). Learning analytics for higher education: proposal of big data ingestion architecture. In SHS Web of Conferences. V.92 (2021). EDP Sciences-Web of Conferences.
  • Ang, K. L. M., Ge, F. L., & Seng, K. P. (2020). Big educational data & analytics: Survey, architecture and challenges. IEEE Access, 8, 116392-116414.
  • Aulakh, K., Roul, R. K., & Kaushal, M. (2023). E-learning enhancement through educational data mining with Covid-19 outbreak period in backdrop: A review. International Journal of Educational Development, 101, 102814.
  • Baker, R. S. J. . d. (2010). Data mining for education. In B. McGaw, P. Peterson, & E. Baker (Eds.), International Encyclopedia of Education, 7(3), 112-118.
  • Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3-17.
  • Chang, R. (2017). Artificial Intelligence to Grow 47.5% in Education over Next 4 Years. THE Journal. Retriewed December 15, 2024 from https://thejournal.com/articles/2017/03/24/ai-market-to-grow-47.5-percent-over-next-four-years.aspx
  • Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22-33.
  • Daniel, B. K. (2017). Big data in higher education: The big picture. Big data and learning analytics in higher education: current theory and practice, 19-28.
  • Eşkinat, A. (2023). Future in higher education: digital university. PhD Dissertation, Isik University, Türkiye.
  • Eskinat, A., & Teker, S. (2023). Digital era for universities: soon or far. PressAcademia Procedia, 17(1), 46-52.
  • Eskinat, A., & Teker, S. (2024). An innovative approach for higher education. PressAcademia Procedia, 19(1), 16-22.
  • Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., ... & Warschauer, M. (2020). Mining big data in education: Affordances and challenges. Review of Research in Education, 44(1), 130-160.
  • Gaftandzhieva, S., Doneva, R., Petrov, S., & Totkov, G. (2018). Mobile learning analytics application: Using Students' big data to improve student success. International Journal on Information Technologies & Security, 10(3), 53-64.
  • Han, J., & Kamber, M. (2006). Classification and prediction. Data mining: Concepts and Techniques, 347-350.
  • Hooda, M., & Rana, C. (2020). Learning analytics lens: Improving quality of higher education. International Journal of Emerging Trends in Engineering Research, 8(5), 241-259.
  • Kaur, K., & Dahiya, O. (2023). Comparison of Various Techniques Implemented for Educational Data Mining and Learning Analytics. In 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (Vol. 10, pp. 824-828). IEEE.
  • Klutka, J., Ackerly, N., & Magda, A. J. (2018). Artificial intelligence in higher education: Current uses and future applications. Louisville:Learning House.
  • Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424.
  • Kumar, A. (2021). National AI policy/strategy of India and China: A comparative analysis. Research and Information System for Developing Countries.
  • Moscoso-Zea, O., Andres-Sampedro, & Lujan-Mora, S. (2016). Datawarehouse design for educational data mining. In 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET), (pp. 1–6).
  • Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22-37.
  • Okewu, E., & Daramola, O. (2017, October). Design of a learning analytics system for academic advising in Nigerian universities. In 2017 International Conference on Computing Networking and Informatics (ICCNI) (pp. 1-8). IEEE.
  • Sabri, R., & Amir, T. S. (2024). Data Management and Analytics in Finance. In Strategic Financial Management: A Managerial Approach (pp. 93-118). Emerald Publishing Limited.
  • Sarala, V., & Krishnaiah, J. (2015). Empirical study of data mining techniques in education system. International Journal of Advances in Computer Science and Technology (IJACST), 4(1), 15-21.
  • Vaidya, A., & Saini, J. R. (2021). A framework for implementation of learning analytics and educational data mining in traditional learning environment. In ICT Analysis and Applications: Proceedings of ICT4SD 2020, V. 2 (pp. 105-114). Springer Singapore.
  • York, T. T., Gibson, C., & Rankin, S. (2015). Defining and Measuring Academic Success. Practical Assessment, Research & Evaluation, 20, 5-16.
There are 27 citations in total.

Details

Primary Language English
Subjects Labor Economics, Microeconomics (Other), Finance, Finance and Investment (Other), Business Administration
Journal Section Articles
Authors

Ali Eskinat This is me 0009-0006-1242-9172

Suat Teker 0000-0002-7981-3121

Publication Date December 31, 2024
Submission Date October 10, 2024
Acceptance Date November 20, 2024
Published in Issue Year 2024 Volume: 20 Issue: 1

Cite

APA Eskinat, A., & Teker, S. (2024). RISING VALUE OF DATA IN CONTEMPORARY HIGHER EDUCATION. PressAcademia Procedia, 20(1), 41-46. https://doi.org/10.17261/Pressacademia.2024.1923
AMA Eskinat A, Teker S. RISING VALUE OF DATA IN CONTEMPORARY HIGHER EDUCATION. PAP. December 2024;20(1):41-46. doi:10.17261/Pressacademia.2024.1923
Chicago Eskinat, Ali, and Suat Teker. “RISING VALUE OF DATA IN CONTEMPORARY HIGHER EDUCATION”. PressAcademia Procedia 20, no. 1 (December 2024): 41-46. https://doi.org/10.17261/Pressacademia.2024.1923.
EndNote Eskinat A, Teker S (December 1, 2024) RISING VALUE OF DATA IN CONTEMPORARY HIGHER EDUCATION. PressAcademia Procedia 20 1 41–46.
IEEE A. Eskinat and S. Teker, “RISING VALUE OF DATA IN CONTEMPORARY HIGHER EDUCATION”, PAP, vol. 20, no. 1, pp. 41–46, 2024, doi: 10.17261/Pressacademia.2024.1923.
ISNAD Eskinat, Ali - Teker, Suat. “RISING VALUE OF DATA IN CONTEMPORARY HIGHER EDUCATION”. PressAcademia Procedia 20/1 (December 2024), 41-46. https://doi.org/10.17261/Pressacademia.2024.1923.
JAMA Eskinat A, Teker S. RISING VALUE OF DATA IN CONTEMPORARY HIGHER EDUCATION. PAP. 2024;20:41–46.
MLA Eskinat, Ali and Suat Teker. “RISING VALUE OF DATA IN CONTEMPORARY HIGHER EDUCATION”. PressAcademia Procedia, vol. 20, no. 1, 2024, pp. 41-46, doi:10.17261/Pressacademia.2024.1923.
Vancouver Eskinat A, Teker S. RISING VALUE OF DATA IN CONTEMPORARY HIGHER EDUCATION. PAP. 2024;20(1):41-6.

PressAcademia Procedia (PAP) publishes proceedings of conferences, seminars and symposiums. PressAcademia Procedia aims to provide a source for academic researchers, practitioners and policy makers in the area of social and behavioral sciences, and engineering.

PressAcademia Procedia invites academic conferences for publishing their proceedings with a review of editorial board. Since PressAcademia Procedia is an double blind peer-reviewed open-access book, the manuscripts presented in the conferences can easily be reached by numerous researchers. Hence, PressAcademia Procedia increases the value of your conference for your participants. 

PressAcademia Procedia provides an ISBN for each Conference Proceeding Book and a DOI number for each manuscript published in this book.

PressAcademia Procedia is currently indexed by DRJI, J-Gate, International Scientific Indexing, ISRA, Root Indexing, SOBIAD, Scope, EuroPub, Journal Factor Indexing and InfoBase Indexing. 

Please contact to contact@pressacademia.org for your conference proceedings.