Skip to main content
Top

2023 | OriginalPaper | Chapter

A Comprehensive Review on Various Data Science Technologies Used for Enhancing the Quality of Education Systems

Author : Olfat M. Mirza

Published in: Intelligent Systems and Machine Learning

Publisher: Springer Nature Switzerland

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Education is one of the major sources for determining the growth of country with high economic development. But the challenges facing by the education systems are poor decision-making ability, high difficulties in adapting new curriculums, inefficient teaching, and training. These factors could inherently affect the performance of education sectors in terms of increased unemployment, reduced workforce, and dissatisfaction outcomes. In order to solve these problems, this research work aims to deploy the data science technologies for improving the learning strategies in education systems. Here, the data mining techniques are mainly used to extract the relevant or useful information from the data and is widely used for solving the higher education problems. Also, this work investigates some of the challenges associated to the deployment of big data in education systems, which includes consequentialism, scientism, privacy, and security. Moreover, operating characteristics and features of the cyber security model are assessed and validated in Sect. 5. Finally, the overall paper is summarized with its obtainment and future work.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Alom, B.M., Courtney, M.: Educational data mining: a case study perspectives from primary to university education in australia. Int. J. Inform. Technol. Comput. Sci. 10(2), 1–9 (2018) Alom, B.M., Courtney, M.: Educational data mining: a case study perspectives from primary to university education in australia. Int. J. Inform. Technol. Comput. Sci. 10(2), 1–9 (2018)
2.
go back to reference Agarwal, R., Dhar, V.: Big data, data science, and analytics: The opportunity and challenge for IS research. 3, INFORMS, 2014, pp. 443–448 Agarwal, R., Dhar, V.: Big data, data science, and analytics: The opportunity and challenge for IS research. 3, INFORMS, 2014, pp. 443–448
4.
go back to reference Gokalp, M.O., Kayabay, K., Akyol, M.A., Eren, P.E., Koçyiğit, A.: Big data for industry 4.0: A conceptual framework. pp. 431–434 Gokalp, M.O., Kayabay, K., Akyol, M.A., Eren, P.E., Koçyiğit, A.: Big data for industry 4.0: A conceptual framework. pp. 431–434
5.
go back to reference Bajpai, N., Biberman, J., Sharma, A.: Information and Communications Technology in the Education Sector in India (2019) Bajpai, N., Biberman, J., Sharma, A.: Information and Communications Technology in the Education Sector in India (2019)
6.
go back to reference Sree, G.S., Rupa, C.: Data mining: performance improvement in education sector using classification and clustering algorithm. Int. J. Innov. Res. Develop. (ISSN 2278–0211), 2(7), 101–106 (2013) Sree, G.S., Rupa, C.: Data mining: performance improvement in education sector using classification and clustering algorithm. Int. J. Innov. Res. Develop. (ISSN 2278–0211), 2(7), 101–106 (2013)
7.
go back to reference Dwivedi, S., Roshni, V.K.: Recommender system for big data in education, pp. 1–4 Dwivedi, S., Roshni, V.K.: Recommender system for big data in education, pp. 1–4
8.
go back to reference Drigas, A.S., Leliopoulos, P.: The use of big data in education. Int. J. Comput. Sci. Issues (IJCSI) 11(5), 58 (2014) Drigas, A.S., Leliopoulos, P.: The use of big data in education. Int. J. Comput. Sci. Issues (IJCSI) 11(5), 58 (2014)
9.
go back to reference Daniel, B.K.: Big Data and data science: a critical review of issues for educational research. Br. J. Edu. Technol. 50(1), 101–113 (2019)CrossRef Daniel, B.K.: Big Data and data science: a critical review of issues for educational research. Br. J. Edu. Technol. 50(1), 101–113 (2019)CrossRef
10.
go back to reference Chweya, R., Ajibade, S.S.M., Buba, A.K. and Samuel, M.: IoT and Big Data Technologies: Opportunities and Challenges for Higher Learning. Int. J. Recent Technol. Eng. (IJRTE) 9(2), 909 (2020) Chweya, R., Ajibade, S.S.M., Buba, A.K. and Samuel, M.: IoT and Big Data Technologies: Opportunities and Challenges for Higher Learning. Int. J. Recent Technol. Eng. (IJRTE) 9(2), 909 (2020)
11.
go back to reference Aldowah, H., Al-Samarraie, H., Fauzy, W.M.: Educational data mining and learning analytics for 21st century higher education: a review and synthesis. Telematics Inform. 37, 13–49 (2019)CrossRef Aldowah, H., Al-Samarraie, H., Fauzy, W.M.: Educational data mining and learning analytics for 21st century higher education: a review and synthesis. Telematics Inform. 37, 13–49 (2019)CrossRef
12.
go back to reference Moscoso-Zea, O., Paredes-Gualtor, J., Luján-Mora, S.: A holistic view of data warehousing in education. IEEE access 6, 64659–64673 (2018)CrossRef Moscoso-Zea, O., Paredes-Gualtor, J., Luján-Mora, S.: A holistic view of data warehousing in education. IEEE access 6, 64659–64673 (2018)CrossRef
13.
go back to reference Bhanti, P., Kaushal, U., Pandey, A.: E-governance in higher education: concept and role of data warehousing techniques. Int. J. Comput. Appl. 18(1), 875–895 (2011) Bhanti, P., Kaushal, U., Pandey, A.: E-governance in higher education: concept and role of data warehousing techniques. Int. J. Comput. Appl. 18(1), 875–895 (2011)
14.
go back to reference Tulasi, B.: Significance of big data and analytics in higher education. Int. J. Comput. Appl. 68(14), 2013 Tulasi, B.: Significance of big data and analytics in higher education. Int. J. Comput. Appl. 68(14), 2013
15.
go back to reference Ahmad, F., Ismail, N.H., Aziz, A.A.: The prediction of students’ academic performance using classification data mining techniques. Appl. Math. Sci. 9(129), 6415–6426 (2015) Ahmad, F., Ismail, N.H., Aziz, A.A.: The prediction of students’ academic performance using classification data mining techniques. Appl. Math. Sci. 9(129), 6415–6426 (2015)
16.
go back to reference Sin, K., Muthu, L.: Application of big data in education data mining and learning analytics--a literature review. ICTACT J. Soft Comput. 5(4), (2015) Sin, K., Muthu, L.: Application of big data in education data mining and learning analytics--a literature review. ICTACT J. Soft Comput. 5(4), (2015)
17.
go back to reference Hasan, N.A., et al.: Business intelligence readiness factors for higher education institution. J. Theor. Appl. Inf. Technol. 89(1), 174 (2016) Hasan, N.A., et al.: Business intelligence readiness factors for higher education institution. J. Theor. Appl. Inf. Technol. 89(1), 174 (2016)
18.
go back to reference Bhise, R.B., Thorat, S.S., Supekar, A.K.: Importance of data mining in higher education system. IOSR J. Human. Social Sci. (IOSR-JHSS), 6(6) 18–21 (2013) Bhise, R.B., Thorat, S.S., Supekar, A.K.: Importance of data mining in higher education system. IOSR J. Human. Social Sci. (IOSR-JHSS), 6(6) 18–21 (2013)
20.
go back to reference Michalik, P., Štofa, J., Zolotova, I.: Concept definition for Big Data architecture in the education system, pp. 331–334 Michalik, P., Štofa, J., Zolotova, I.: Concept definition for Big Data architecture in the education system, pp. 331–334
21.
go back to reference Manohar, A., Gupta, P., Priyanka, V., Uddin, M.F.: Utilizing big data analytics to improve education. Manohar, A., Gupta, P., Priyanka, V., Uddin, M.F.: Utilizing big data analytics to improve education.
22.
go back to reference Johnson, J.A.: The ethics of big data in higher education, the international review of information. Ethics 21, 3–10 (2014) Johnson, J.A.: The ethics of big data in higher education, the international review of information. Ethics 21, 3–10 (2014)
23.
go back to reference Li, Y., Zhai, X.: Review and prospect of modern education using big data. Procedia Comput. Sci. 129, 341–347 (2018)CrossRef Li, Y., Zhai, X.: Review and prospect of modern education using big data. Procedia Comput. Sci. 129, 341–347 (2018)CrossRef
24.
go back to reference Taylor, E., Goede, R.: Using critical social heuristics and project-based learning to enhance data warehousing education. Syst. Pract. Action Res. 29(2), 97–128 (2016)CrossRef Taylor, E., Goede, R.: Using critical social heuristics and project-based learning to enhance data warehousing education. Syst. Pract. Action Res. 29(2), 97–128 (2016)CrossRef
25.
go back to reference Sedkaoui, S., Khelfaoui, M.: Understand, develop and enhance the learning process with big data. Inform. Disc. Delivery 47(1), 2–16 (2018) Sedkaoui, S., Khelfaoui, M.: Understand, develop and enhance the learning process with big data. Inform. Disc. Delivery 47(1), 2–16 (2018)
26.
go back to reference Ptiček, M., Vrdoljak, B.: Big data and new data warehousing approaches, pp. 6–10 Ptiček, M., Vrdoljak, B.: Big data and new data warehousing approaches, pp. 6–10
27.
go back to reference Santoso, L.W.: Data warehouse with big data technology for higher education. Proc. Comput. Sci. 124, 93–99 (2017)CrossRef Santoso, L.W.: Data warehouse with big data technology for higher education. Proc. Comput. Sci. 124, 93–99 (2017)CrossRef
28.
go back to reference Khatibi, V., Keramati, A., Shirazi, F.: Deployment of a business intelligence model to evaluate Iranian national higher education. Social Sci. Human. Open 2(1), 100056 (2020) Khatibi, V., Keramati, A., Shirazi, F.: Deployment of a business intelligence model to evaluate Iranian national higher education. Social Sci. Human. Open 2(1), 100056 (2020)
29.
go back to reference Guster, D., Brown, C.G.: The application of business intelligence to higher education: technical and managerial perspectives. J. Inform. Technol. Manage. 23(2), 42–62 (2012) Guster, D., Brown, C.G.: The application of business intelligence to higher education: technical and managerial perspectives. J. Inform. Technol. Manage. 23(2), 42–62 (2012)
30.
go back to reference Alpar, P., Schulz, M.: Self-service business intelligence. Bus. Inf. Syst. Eng. 58(2), 151–155 (2016)CrossRef Alpar, P., Schulz, M.: Self-service business intelligence. Bus. Inf. Syst. Eng. 58(2), 151–155 (2016)CrossRef
Metadata
Title
A Comprehensive Review on Various Data Science Technologies Used for Enhancing the Quality of Education Systems
Author
Olfat M. Mirza
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-35081-8_30

Premium Partner