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ISSN 2227-6017 (ONLINE), ISSN 2303-9868 (PRINT), DOI: 10.18454/IRJ.2227-6017
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Kozlova I.V. et al. "ANALYTICS IN THE THEORETICAL SPECTRUM OF DIGITALIZATION OF HIGHER EDUCATION INSTITUTIONS BASED ON A REVIEW OF FOREIGN REFUGE SOURCES". Meždunarodnyj naučno-issledovatel’skij žurnal (International Research Journal) № 4 (106) Part 4, (2021): 123. Mon. 19. Apr. 2021.
Kozlova I. V. ANALYTICS IN THE THEORETICAL SPECTRUM OF DIGITALIZATION OF HIGHER EDUCATION INSTITUTIONS BASED ON A REVIEW OF FOREIGN REFUGE SOURCES / I. V. Kozlova, M. B. Saidakhmedova // Mezhdunarodnyj nauchno-issledovatel'skij zhurnal. — 2021. — № 4 (106) Part 4. — С. 123—126. doi: 10.23670/IRJ.2021.106.4.127




Обзорная статья

Козлова И.В.1, *, Саидахмедова М.Б.2

1 ORCID: 0000-0003-0940942X;

2 ORCID: 0000-0002-1063-6825;

1, 2 Российский экономический университет им. Г.В. Плеханова, Москва, Россия

* Корреспондирующий автор (Kozlova.IV[at]


Данная статья представляет обобщение теоретического опыта использования методов, решений и инструментов для цифровизации образования. Основное внимание уделяется внедрению результатов анализа больших данных и бизнес-аналитики в бизнес-процессы высших учебных заведений. Обзор выполнен по материалам зарубежных публикаций и сайтов высших учебных заведений Сербии, Великобритании, Турции, Индии. На концептуальном уровне определены виды работ по пилотным проектам аналитики больших. Сделан вывод о том, что цифровая трансформация является одной из сложных моделей трансформации деятельности высшего учебного заведения на основе применения цифровых технологий.

Ключевые слова: цифровизация, высшее образование, аналитика, информационные технологии, обучающая аналитика, интеллектуальный анализ образовательных данных, EDM, пользовательское моделирование.


Review article

Kozlova I.V.1, *, Saidakhmedova M.B.2

1 ORCID: 0000-0003-0940942X;

2 ORCID: 0000-0002-1063-6825;

1, 2 Plekhanov Russian University of Economics, Moscow, Russia

* Corresponding author (Kozlova.IV[at]


This article presents a generalization of the theoretical experience of using methods, solutions and tools for the digitalization of education. The main focus is on integrating the results of big data analysis and business intelligence into the business processes of higher education institutions. The review is based on materials from foreign publications and websites of higher educational institutions in Serbia, Great Britain, Turkey, India. At the conceptual level, the types of work on pilot projects of large analytics are defined. It is concluded that digital transformation is one of the complex models for transforming the activities of a higher educational institution based on the use of digital technologies.

Keywords: digitalization, higher education, analytics, information solutions, learning analytics, educational data mining, EDM, user modeling.


Today’s university leaders and faculty must reimagine higher education in a digital-dominated new world. In the wake of technology, jobs and competencies change faster than people or organizations can adapt. According to research by the World Economic Forum, the basic skills required for most professional duties will change by an average of 42% by 2022 [1, P. 20-27].

Anticipating changes of this magnitude, companies are urgently trying to find and acquire the competencies they need to stay competitive. So, the framework of their own interests should not limit universities, they need to strive to create an ecosystem and use online education to expand their audience and establish partnerships with other universities and educational service providers. With digital technologies support, it is possible to create a unified learning ecosystem, as well as supplement your own educational programs with the best courses from other educational institutions.

Digital ecosystems make it possible to invite and connect experts from scientists or business representatives to offer students individual study programs anywhere in the world. Other opportunities include online faculty exchange with other universities, for cooperation of university research.

Business intelligence can bring significant benefits to universities by enabling data-driven decisions: know what is happening (descriptive analytics), what is to happen in the future (predictive analytics), investigate trends, causes, forecasts, (prescriptive analytics).

According to the work [2, P. 187], these changes can be divided into seven areas: players, institution business models, course models, learning data and analytics, cost, measurement of success, and threats to credentials. Different authors define digital transformation in different, often controversial, ways. The most common viewpoints include: individual, institution / organization, Network, industry or entire ecosystem, society or economy, and the digital era [5, P. 30-35], [6, P. 7-10]. According to most authors, digital business transformation has six components [7, P. 100-102];

  • Established and adopted organizational strategy for digitalization innovations;
  • Organized, flexible, fast and highly adaptable joint processes to modern business models;
  • Full automation of business processes;
  • Detailed analysis and research of customer decision making;
  • Information technologies for all organizational business processes;
  • Useful and relevant data and data analytics.

Authors from Serbian University of Novi Sad, University of Belgrade, The School of Electrical and Computer Engineering of Applied Studies, Belgrade agree that digital transformation implies technological and organizational changes with digital technologies. [3, P. 9495], [4, P. 120-125].

The study has some limitations. Since the study was conducted on a small sample of foreign universities, this is far from a generalization, but it can still be considered as a starting point for further research in the spectrum of implementation of digital transformation strategies at leading universities.

Analysis of the tasks and strategies of digital transformation related to research and social services, carried out in 18 universities in Turkey [12, P.15], showed a rather low level of mission of Turkish universities. Turkish universities generally perceive digital transformation as a technological tool for the implementation of distance and open education.

The digitalization of education in India allows the creation of new business models – virtual university, smart university, digital university, e-university, flexible university, university 4.0 and so on. Universities will have to think more about their products and think about effective ways to deliver them to today’s students, most of which will require technology integration [13].

Educational data mining (EDM) and learning analytics (LA) are used for and model building in online learning systems. One of the areas is user modeling, which includes: assessment of student knowledge, student behavior and motivation, user experience, user satisfaction. An analyst can detect if a student is wandering and bring him back to the course. In difficult cases, it is possible to detect weariness by keywords patterns and use clicks to control the student’s attention. As data is collected in real time, there is an opportunity for improvement with feedback loops operating on different timelines: immediately, daily, monthly, and annually for education quality improvement [6, P. 5]. The same types of data can be used for user profiling or grouping similar students into categories based on their main characteristics.

Some data mining and analytics applications are designed for experimental modeling of a problem structure representation [8, P. 340], [9, P. 10]. Many studies are devoted to the of mathematical methods in EDM. Among them: descriptive statistics, detailed statistical analysis such as inferential, Bayesian, t-test, interpretive analysis and classification [10, P. 3], [11, P. 172].

EDM and LA are based on data mining, machine learning, and statistical techniques to uncover patterns in large educational data sets. Deep learning (DL) becomes popular with educational researchers. It is based on neural network architectures and used in the image recognition and natural language processing [7, P. 103]. Articles on visual learning analytics have shown that traditional histograms and scatter charts are still popular. Some studies focus on complex visualizations, while others focus on educational theories. There is a lack of research on the simultaneous use of complex visualizations and effective methods, technologies, and educational content. [8, P. 335], [9, P. 8]. The literature overview shows that various methods are used in learning analytics (LA): social media research, semantic data analysis, forecasting, clustering, predictive analytics, target vectors, an individual learning trajectory, teaching efficiency improving, graduate’s employment, research efficiency improving. Problems have been identified: collection, accumulation, evaluation, and analysis of data; lack of relations between academic disciplines; the infrastructure and environment improving; questions of behavior and safety.

Analytics Methods in Digital Education

The research method is based on a system-structural approach to the problems under consideration. The main goal of the digital education is to update and innovate services and operational processes of universities. There are three ways to do this. The first is to transform services before improvements and changes for operations making, that is, activities are considered within processes. The second is related to identifying new and making changes to existing digital processes. The third includes these methods and is mainly associated with all services integration [2, P.190].

The authors of the article adhere to the second of these approaches to education digitalization. A four levels business process model was proposed, arranged hierarchically: Global processes, main processes, sub-processes, actions, and tasks. Global processes: learning and teaching process, research process, providing processes, planning, and management process. Main processes for redefining the educational, research and administrative services of higher school institutions are shown in Table 1.


Table 1 – Global & main processes in higher school institutions

Learning and teaching process Research process
study programs accreditation research planning
educational process preparation and implementation research preparation
educational process results monitoring researching
educational process assessment research results monitoring
student and teacher mobility implementation research evaluation
Providing processes Planning and management processes
student administration services organization management services
library services change and business process management
recruitment and development services development plan development
finance and accounting services budget and funds planning
marketing, sale and distribution services efficiency mark
procurement management  

Note: adopted from [3]


The key factor that determines whether organizations will stay afloat in today’s fast-growing business environment is their ability to harness the potential and power of data by gathering useful ideas for decision making and innovation.

Presently, the focus of business intelligence is reporting. At the same time, well-structured reports are created by analysts and disseminated to the entire department. The trend is to provide effective data analysis tools to all employees in the organization. This approach allows companies to become analysts themselves.

Analytic platforms provide the basis for decision making. The future belongs to applications and services based on open public data with large-scale government control. Companies also can use open-source methods and open platforms to collect data, generate useful ideas, and innovate. Numerical methods have long been the backbone of analytics and business. In practice, unstructured data is often used to make informed decisions. Modern technological solutions allow the use of analytical methods to extract information from large volumes of hybrid data. This forms the basis for the analysis of big data in integrated forms and web analytics [7, P. 101], [9, P. 17].

In many organizations there is a gap between the sheer volume of data and the managers who make and implement business decisions. Analytics can fill this gap by recommendations developing for the best practice implementation of business solutions in a user-friendly format.

At the same time organizations that have historically invested heavily in technology solutions for data management and analytics should not lose sight of the key technologies that impact their business: blockchain technology; unmanned devices; Internet of things; robots; 3D printing; virtual reality; supplemented reality.

To illustrate the feasibility and scope of analytics adoption in universities, this article discusses three areas that are suitable for pilot projects: tracking admissions, student recruitment optimizing and academic consulting.

Universities of all types and sizes always strive to recruit and retain excellent students. To answer questions about where and how to conduct outreach or which students to accept and award scholarships, it is important to have a deep understanding of the students applying level, also which ones will actually enroll and be successful. Analytics can provide educational institutions with a model for improving organizational performance, admission and enrollment decisions making. Analytical solutions will enable to answer a variety of questions: how prospective students and parents interact with websites or demographic changing.

Analytical solutions usually offer extensive libraries. Users can embed statistical metrics into their analytic workflow from basic generalization for forecasting optimization. Important data types about website visitors and job seekers can help universities understand what people are looking for and shape their marketing strategy and curriculum. Admission staff can perform data mining and create highly advanced statistical algorithms. This will help to improve admission efficiency and ensure that educational services meet the needs of universities, students, and employers.

In the strategic planning of educational tasks, the university relies on the current behavior and activity of students. An appropriate analytical solution can speed up this process and improve accuracy, while the university authorities detail the data on student preferences. Analytical applications can correspond to academic programs of higher education. Using filters, it is possible to analyze the departments, the popularity of subjects and teachers, the quality of teaching. Teachers and management can collaborate and find out which specialties are popular with students. These data will help universities in strategic and tactical planning of the educational process, and in specialized programs development that take students interests into account.

With the ubiquitous availability of real-time data, analytics, and machine learning techniques, the digital twin could be an innovative key decision-making tool in the future. A digital twin is a realistic digital representation of a real object, asset, process, or system. Higher education planning could be revolutionized by improving the decision-making power of digital twin technology, researchers from the UK emphasize [14].

Universities continually collect data about their students and graduates. The next step is to turn this data into useful information. Analytics in academic consulting helps to improve your progress from enrollment to graduation. Complex data collected during training and information management systems can be translated into real time. These ideas help students make informed decisions when they have different questions: tuition fees, length of study, and the time it takes to get a degree.


As a result of the research, it was concluded that in the higher education field, digital transformation is one of the complex models of the university’s activities transformation based on digital technologies. The creation of a new intelligent business model can lead to significant changes. One effective information-driven approach is to use historical, current, and predictive analytics for the best representation of education institutions’ data based on value and outcomes.

The aim was to highlight the importance of analytics in higher education institutions as the most important component of their business model. Analytics at the university will help to significantly improve the quality of decisions, based on internal and external data. Data management analytics, student analytics (including student sentiment), staff analytics, and other specialized analysts can play a key role in proactively competing and ensuring a safer future for educational institutions. The growing offer of open-source platforms and tools enables analytics (generate useful ideas, innovation) in higher education to improve its quality, which makes education valuable.

The review performed may be helpful to educators, course developers, and business leaders in Russian Universities of Economics.

Конфликт интересов

Не указан.

Conflict of Interest

None declared.

Список литературы / References

  1. Aldowah H. Educational data mining and learning analytics for 21st century higher education: A review and synthesis / H. Aldowah, H. Al-Samarraie and W.M. Fauzy // Telematics and Informatics. – 2019. – V. 37. – P. 13–49.
  2. Sandkuhl K. Digital Transformation in Higher Education – The Role of Enterprise Architectures and Portals / K. Sandkuhl, H. Lehmann // Digital Enterprise Computing. – 2017. – P. 181-200. doi: 10.1109/EDOCW.­2015.16
  3. Seres L. Digital Transformation in Higher Education / L. Seres, V. Pavlicevic, P. Tumbas // Proceedings of INTED2018 Conference. – 2018. – P.9491 – 9497. doi: 10.21125/inted.2018.2348
  4. Gasevic D. Detecting learning strategies with analytics: Links with self-reported measures and academic performance / D. Gasevic, J. Jovanovic, A. Pardo // Journal of Learning Analytics. – 2017. –V.4. – P. 113–128. doi: 10.18608/jla.2017.42.10
  5. O’Dowd. I. Using learning analytics to improve online formative quiz engagement/ I. O’Dowd // Irish Journal of Technology Enhanced Learning. – 2018. – V.3. – P. 28-41. doi: 10.22554/ijtel.v3i1.25
  6. Aljohani N.R. An integrated framework for course adapted student learning analytics dashboard / N.R. Aljohani, Al Daud, R.A. Abbasi // Computers in Human Behavior. – 2018. –V. XXX. – P. 1-12. doi: 10.1016/j.chb.2018.03.035
  7. Viberg O. The current landscape of learning an-alytics in higher education / O.Viberg, M. Hatakka, O. Balter // Computers in Human Behavior. – 2018. – V. 89. – P. 98-110. doi: 10.1016/j.chb.2018.07.027
  8. Fernandez M. PBIS in Georgia Department of Juvenile Justice: Data dashboard and radar reports utilized for team data-based decision making with facility team leader perspectives / M. Fernandez, D. McClain, B. Brown-Williams et al. // Residential Treatment for Children. – 2015. –V. 32. –P. 334-343. doi: 10.1037/0735-7028.39.4.396
  9. Ndukwe I.G. Data science approach for simulating educational data: Towards the development of teaching outcome model (TOM) / I.G.Ndukwe, B.K.Daniel, & R.Butson // Big Data and Cognitive Computing. – 2018. –V. 24. –P. 1-18. doi: 10.3390/bdcc5010008
  10. Sedrakyan G. Linking learning behavior analytics and learning science concepts: Designing an learning analytics dashboard for feedback to support learning regulation / G.Sedrakyan, J.Malmberg, K.Verbert et al. // Computers in Human Behavior. – 2018. –V. XXX. –P. 1-15. doi: 10.1016/j.chb.2018.06.005
  11. Smith M. Output from statistical predictive models as input to eLearning dashboards / M. Smith // Future Internet. – 2015. –V. 7. – P. 170-183. doi: 3390/fi7020170
  12. Kuzu Ö.H. Digital Transformation in Higher Education: A Case Study on Strategic Plans / Ö.H. Kuzu // Vysshee obrazovanie v Rossii. 2020. V. 29. № 3. P. 9-23. doi: 10.31992/0869-3617-2019-29-3-9-23
  13. Shantanu R. Creating new business models / R. Shantanu, K. Srimathi. [Electronic resource]. URL: (accessed 13.03.2021)
  14. Barn B. The digitalisation of the Middlesex University / B. Barn. [Electronic resource]. URL: (accessed 13.03.2021)

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