Massification, digitalization and bureaucratization are now the major trends that shape higher education. Massification has led to an inevitable problem of the heterogeneity of students and the need for adaptive learning; digitalization has created a need for distance learning technologies and, as a result, learning data production; finally, bureaucratization has meant that the education quality assessment now predominantly relies on quantitative rather than qualitative indicators. At the crossing of these trends, a new research interest has emerged, which develops both theoretical and practically oriented studies and which has become known as learning analytics. Learning analytics is now actively discussed in Western countries, where national policies to regulate and stimulate this sphere are designed and professional associations of specialists in learning analytics are created. Proponents of learning analytics believe that the data collected and analyzed by an education institution will help the management take more justified and objective decisions than those based on expert opinions. Learning analytics is understood in this paper as a necessary tool for detecting the weak sides of the curricula. It also helps build students’ individual learning trajectories, which is essential for an individualized approach in education and for making the learning process more adaptive. Opponents of learning analytics, in their turn, see it as a threat to the current balance of power in education, the roles of the teacher and manager, and point out the need for specific competencies and the danger of personal data breach. Russia is now left out of the global agenda: except for a few recent cases, learning analytics is still viewed by many as more of a promise than reality. This review is aimed at shedding light on the modern understanding of learning analytics, its development in the world and in Russia, the prospects and limitations of its application in Russia from the perspective of the key stakeholders in higher education. We also propose recommendations regarding the organization of a university learning analytics system. This article will be of interest to university managers and decision-makers, teachers and scholars of higher education as it provides information on the organization of a data management system, including the collection, analysis and use of data.
REPROGRAMMING CORRECTIONAL EDUCATION: A CONCEPTUAL FRAMEWORK FOR THE IMPLEMENTATION OF ADAPTIVE LEARNING TECHNOLOGIES IN PRISONS
