Adaptive education and Open Educational Resources in vocational education and training
This article is devoted to the issue of developing adaptive learning systems for vocational education and training (VET). Firstly, it justifies the urgency of developing and using personalized adaptive learning in vocational educational organizations. Specific features of the Russian VET system and its students are described, demonstrating a number of arguments for the importance of a search for new digital educational solutions. Secondly, the paper elaborates on the theoretical framework of personalization of vocational education and training, which takes into account the necessity for both skills and knowledge. Finally, the authors present a prototype of an adaptive educational system, which is based on ontologically-controlled management of learning trajectories. The developed software is aimed at improving the effectiveness of the VET material science curriculum.
These day adaptivity is the cutting edge of modern education. Technologies are being developed rapidly and bringing new possibilities to educators. Thus, diverse types of adaptive learning environment have appeared during these last decades. Material Science and Engineering Education (MSEE) have a solid formalized foundation, which consists of standards, recommendations and clear rules. Moreover, investigators report on growing role of computer in teaching and learning in MSEE. These brings great perspectives to computer adaptive learning system based on a material science and engineering ontology. This paper aims to justify general pedagogical foundations of adaptivity and to collect requirements to a computer adaptive learning system. As an extra result we introduce the architecture of ontology-based adaptive learning system to MSEE.
Upgrading skill formation has become an increasingly urgent task for societies facing the challenges of rapid technological change and globalization. However, reform of systems of vocational education and training (VET) poses severe challenges for aligning the interests of schools, firms, households, and governments, even in societies with relatively efficient markets for labor and education. Where market institutions are poorly developed, these challenges are particularly acute, resulting in endemic mismatches between the supply and demand of skill. Currently governments in many countries, including the United States, Russia, and China, are seeking to adopt elements of the German dual education model. The Russian federal government has undertaken several initiatives designed to upgrade VET by encouraging closer cooperation of vocational schools and firms at the regional level, including the adoption of dual education programs. This paper focuses on one such project: a 2013 pilot program administered by the Russian Agency for Strategic Initiatives, to foster the development of new models of dual education. The paper compares the 13 pilot regions with regions that submitted proposals but were not selected and with all other regions along multiple economic, social, demographic, and institutional dimensions. The findings suggest hypotheses about the conditions that enabled the pilot regions to take advantage of federal policies encouraging the adoption of dual education. More generally, the paper sheds light on institutional solutions to collective action dilemmas in skill formation in transitional and developing societies.
Taking into account money and other resources invested into massive open online courses (MOOCs) production universities face a challenge of MOOCs integration into higher and further professional education. Is university able to warrant its investments into MOOCs? What decisions should a university consider: selling certificates to MOOC completers, launching chargeable MOOC specializations or online degree programs? How to adapt MOOCs to the needs of a broad audience so that both on-campus students and international learners could benefit from it including these courses into their individual curricula? Tomsk State University (TSU) works in an effort to solve this problem. This paper is devoted to the model of organizing e-learning in a classical university basing on MOOCs and its integration into the system of lifelong education, as well as to the steps of maintaining this complex process in the framework of current trends in e-learning.
This study is devoted to different types of students’ behavior before they drop an adaptive course. The Adaptive Python course at the Stepik educational platform was selected as the case for this study. Student behavior was measured by the following variables: number of attempts for the last lesson, last three lessons solving rate, the logarithm of normed solving time, the percentage of easy and difficult lessons, the number of passed lessons, and total solving time. We applied a standard clustering technique, K-means, to identify student behavior patterns. To determine optimal number of clusters, the silhouette metrics was used. As the result, three types of dropout were identified: “solved lessons”, “evaluated lessons as hard’’, and “evaluated lessons as easy”.
Around the world, governments, educators and employers have expressed growing interest in German-style methods of technical and vocational education and training (TVET). In such countries, schools and firms share responsibility for providing technical and vocational education, a model often called the ‘dual system of vocational training and education.’ The dual system means that occupational training occurs at two linked sites, educational institutions and workplaces. Dual education aims at matching the demands of a dynamically changing economy with the skill profiles of those graduating from educational institutions. To a large extent, dual education systems enable young people to acquire not simply technical and occupational skill, but broadly defined competencies that serve as the foundation for rewarding careers and social esteem. Little wonder that many countries have turned with renewed interest to the dual TVET system. However, actual implementation of the dual system outside the core Germanic countries in Europe has proven to be extremely challenging. Successful examples of institutional transplantation are rare. However, in some countries, local partnerships embracing elements of dual education have formed, uniting educational institutions, government entities and firms in partnerships to upgrade TVET. This paper discusses some of the characteristic patterns of such partnerships in the U.S.
If private sector agents update their beliefs with a learning algorithm other than recursive least squares, expectational stability or learnability of rational expectations equilibria (REE) is not guaranteed. Monetary policy under commitment, with a determinate and E-stable REE, may not imply robust learning stability of such equilibria if the RLS speed of convergence is slow. In this paper, we propose a refinement of E-stability conditions that allows us to select equilibria more robust to specification of the learning algorithm within the RLS/SG/GSG class. E-stable equilibria characterized by faster speed of convergence under RLS learning are learnable with SG or generalized SG algorithms as well.
The development of mass higher education resulted in increased enrollments, where each student has his own preferences on ways he organizes the learning process. In this case, digital technologies serve as a tool for adapting educational resources to students’ needs. However, little is known whether adaptive learning can transform the educational system and help students to learn more effectively. In this review paper, we aim to explore existing practices of adaptive learning in higher education. The review is divided into three parts. The first part deals with algorithms of adaptive learning and gives examples of adaptive learning systems. The second part reviews the evidence for the effectiveness of adaptive learning. The final part includes a discussion of the main problems of adaptive learning systems. This issue will be of interest to researchers and managers dealing with the digital transformation of education.