The digital economy is now becoming a framework in which the processes of transformation of education and science are conceptualized. A characteristic feature of the digital economy is “the maximum satisfaction of the needs of all its participants through the use of information, including personal information.
This becomes possible thanks to the development of information, communication and financial technologies, as well as the availability of infrastructure, which together provide the possibility of full interaction in the hybrid world of all participants in economic activity: subjects and objects of the process of creation, distribution, exchange and consumption of goods and services. The digital economy is based on a change in the technological order, the transformation of traditional markets under the influence of the widespread penetration of digital technologies, a radical change in the division of labor, transformation of jobs, the creation of new business models, a significant increase in labor productivity and investment in new technologies.
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Future trends and technologies in teaching and training
The analytical report 2020 EDUCAUSE Horizon report: Teaching and learning edition highlights the following trends that determine the state and near future of higher education.
Social: These include the growing focus on student well-being and mental health. Also of great importance are demographic processes and the associated change in the image of a typical student. The issues of fairness and equity in access to education are becoming more and more significant.
Economic: Among them, the rise in the cost of higher education, the transformation of the future labor market and skills, and the demand for certain general and specialized competencies of graduates are distinguished. Climate change is also affecting campuses and their policies, as well as the emergence of new expert interdisciplinary educational programs in this area.
Political: First of all, there is a continuing decline in funding for higher education and the need for universities to find new sources of funds, increase competitiveness, and produce new paper. With the general trend of decreasing the value of higher education due to the emergence and proliferation of alternative ways of obtaining education, this challenge is becoming especially acute.
Trends in the field of higher education itself. There have been changes in the composition and number of students affecting recruitment policies and services. Alternative ways of getting education are developing, including online learning is gaining momentum.
The same analytical report also records trends in educational technologies that have the greatest potential for transforming education in the present and future.
Adaptive learning is a system that, by analyzing data about a student, shows that students know how ready they are for new knowledge and advancement to a more complex level, as well as how their abilities develop over time, on the basis of which an assumption is made about how build a more or less correct individual route. Adaptive technologies are gradually turning into one of the main educational technological tools aimed at expanding the practice of individualization of learning.
The use of this technology is still on the upward part of the new technology adoption curve, but even at this early stage, it can provide educational institutions with the opportunity to strategically rethink curricula and even entire curricula in the context of increasing the effectiveness of learning.
There are encouraging results in universities that have adopted adaptive learning holistically: in many cases, both the academic success of students and their satisfaction with their learning experience have increased. Examples include the University of Florida, which developed an adaptive Spanish course; The University of Arizona, which has created an adaptive learning system for all academic disciplines in the framework of obtaining a bachelor’s degree in biology
Artificial intelligence (AI) and machine learning (ML). AI is understood as “an attempt to create machines that can do what was previously possible only through human cognition”. ML is like “machine learning something new without special programming”. Both ML and AI are increasingly infiltrating higher education, such as Learning Management Systems (LMS), Student Information Systems (SIS), office applications, libraries and admissions offices, automatic captioning systems, and mobile applications.
AI is capable of low-order routine operations and repetitive cognitive tasks instead of humans. Moreover, many of these systems will be able to learn over time, increasing their accuracy and speed. Examples include Google Assistant demonstrating the potential of text-to-speech and natural language processing; digital teaching assistants (test checking, borrowing detection), bot for college library users (University of Oklahoma).
Learning analytics. Over the past decade, many higher education institutions have focused their missions and strategic planning around student achievement through effective practices that contribute to the success of their papers. It has become critical to have tools that measure, collect, analyze and report information about student performance. It uses data at the course level, such as scores from the LMS, institutional-level information from student information systems, registrar records, financial data, information from research units.
The data depends on the campus itself, on the availability of technical tools for collecting and analyzing them, and on the financial capabilities of the institution. The tools can be used both ready-made and individually developed for the needs of a particular institution. The collection and study of data for educational analytics generates ethical, political and philosophical debates. Examples of such practices are Berkeley Online Advising, a digital footprint and predictive analytics platform at the University of Berkeley, and Elements of Success, an analytic dashboard for students at the University of Iowa.
XR, VR, AR and other technologies. Augmented Reality (XR) is an overarching term for environments that either combine the physical world with the virtual world or provide a fully immersive virtual experience. The most common technologies are augmented reality (AR) (overlaying virtual and physical environments) and virtual reality (VR, which gives the experience of manipulating virtual objects in a completely virtual environment and interacting in it). Most often, an immersive experience is achieved with a dedicated headset, but for AR, a smartphone is sufficient. Another type of XR is holography, which renders an object as a three-dimensional image.
Higher education is actively experimenting with XR, and despite current hurdles (such as the cost of equipment and the effort it might take to get an assignment done), XR’s potential as a learning tool is high. However, the attitude towards technology is still vary. In most cases, an educational institution will set up a laboratory or center as the site for initial XR research. For example, the University of Leeds uses XR technology in medical dental education.
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