Новые формы высшего образования с использованием современных онлайн-технологии

The University is regarded as a special social institution fulfilling a number of important social task: the production and broadcasting of knowledge and scientific picture of the world; reproduction and development of human capital; socialisation of the young generation; providing the economy with personnel qualifications, etc. and in various cultural and historical periods of the functions of the University have a distinctive focus, for example, for industrial companies is training and qualified individuals for the postindustrial society - the notion of «human capital». Education has changed dramatically in recent decades. Now the learning process is very difficult to imagine without the Internet. The main purpose of mass open online courses is to provide students with direct access to educational materials without the need to enter an educational institution. As you know, education is an important social process, so it should be as accessible to everyone as possible. Mass open online courses, implemented with the participation of the world S leading universities, are developing dynamically on a global scale, expanding access to education and lifelong learning for people from all over the world and having a significant impact on the modern higher education system. The study of the courses is of a recommendatory nature, can be offered for independent study, without requirements for monitoring the results. The article discusses the pros and cons of online education, the problems of entering the Russian educational market.Университет рассматривается как особый социальный институт, выполняющий ряд значимых для общества задач: производство и трансляцию знаний и научной картины мира; воспроизводство и развитие человеческого капитала; социализацию молодого поколения; обеспечение экономики кадрами высокой квалификации и др. Причем в различные культурно-исторические периоды функции университета имеют характерные фокусировки, например, для индустриального общества - это подготовка кадров и квалифицированных специалистов, для постиндустриального общества - используется понятие «человеческий капитал». За последние десятилетия образование кардинально изменилось. Теперь процесс обучения очень сложно представить без Интернета. Основная цель массовых открытых онлайн-курсов - обеспечение прямого доступа студентов к учебным материалам без необходимости поступления в образовательное учреждение. Как известно, образование является важным социальным процессом, поэтому оно должно быть максимально доступно каждому человеку. Массовые открытые онлайн-курсы, реализуемые при участии ведущих университетов мира, динамично развиваются в глобальном масштабе, расширяя доступ к образованию и обучению в течение всей жизни для людей со всего мира и оказывая существенное воздействие на современную систему высшего образования. Изучение курсов носит рекомендательный характер, может быть предложено для самостоятельного изучения, без требований к контролю результатов. В статье рассмотрены плюсы и минусы онлайн-образования, проблемы вхождения на российский образовательный рынок

SandBOX: An intuitive conceptual design system

We describe the development and use of a new conceptual design system, called SandBOX, which combines a range of intuitive interfaces with real-time analysis, thus enabling a wide variety of users to develop performative concept designs. We show how this interactive design platform can overcome some of the limitations of current physical model-based design processes, whilst retaining many of their advantages

What Supports Serendipity on Twitter? Online Survey on the Role of Technology Characteristics and Their Use

Serendipity experiences are highly desirable in work life, considering both individuals' learning and organizational innovation capacity. This study looks into information and social serendipity in the context of Twitter. While Twitter can be viewed as a fruitful platform for serendipity to emerge, there is little understanding of what technology characteristics and use practices contribute to such experiences in work-related use. Drawing from the functional affordances theory, the paper investigates the role of presenteeism, self-disclosure, recommendation quality and pace of change, and different types of Twitter use as possible antecedents of serendipity. A cross-sectional international online survey was conducted with 473 respondents who actively use Twitter in their work. An exploratory factor analysis was performed, followed by linear regression analysis to identify relevant statistical associations. The findings indicate that presenteeism (i.e., the fundamental element of reachability) seems to have an effect on serendipity while the more designable characteristics, like the quality of recommendations, do not. Overall, the findings imply that serendipity experiences are primarily explained by individual characteristics like personality and specific ways of using Twitter. This is amongst the first studies on the role of Twitter characteristics as functional affordances in the formation of serendipity. The extensive empirical study contributes a detailed analysis of the antecedents of serendipity and opens avenues for research and design to identify new serendipity-inducing mechanisms.publishedVersionPeer reviewe

Playfulness and progression in technology-enhanced social experiences between nearby strangers

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Design Patterns for Augmented Reality Learning Games

Augmented Reality (AR) is expected to receive a major uptake with the recent availability of high quality wearable AR devices such as Microsoft’s Hololens. However, the design of interaction with AR applications and games is still a field of experimentation and upcoming innovations in sensor technology provide new ways. With this paper, we aim to provide a step towards the structured use of design patterns for sensor-based AR games, which can also inform general application development in the field of AR

DataSHIELD – new directions and dimensions

In disciplines such as biomedicine and social sciences, sharing and combining sensitive individual-level data is often prohibited by ethical-legal or governance constraints and other barriers such as the control of intellectual property or the huge sample sizes. DataSHIELD (Data Aggregation Through Anonymous Summary-statistics from Harmonised Individual-levEL Databases) is a distributed approach that allows the analysis of sensitive individual-level data from one study, and the co-analysis of such data from several studies simultaneously without physically pooling them or disclosing any data. Following initial proof of principle, a stable DataSHIELD platform has now been implemented in a number of epidemiological consortia. This paper reports three new applications of DataSHIELD including application to post-publication sensitive data analysis, text data analysis and privacy protected data visualisation. Expansion of DataSHIELD analytic functionality and application to additional data types demonstrate the broad applications of the software beyond biomedical sciences

Facilitating Organisational Fluidity with Computational Social Matching

Striving to operate in increasingly dynamic environments, organisations can be seen as fluid and communicative entities where traditional boundaries fade away and collaborations emerge ad hoc. To enhance fluidity, we conceptualise computational social matching as a research area investigating how to digitally support the development of mutually suitable compositions of collaborative ties in organisations. In practice, it refers to the use of data analytics and digital methods to identify features of individuals and the structures of existing social networks and to offer automated recommendations for matching actors. In this chapter, we outline an interdisciplinary theoretical space that provides perspectives on how interaction can be practically enhanced by computational social matching, both on the societal and organisational levels. We derive and describe three strategies for professional social matching: social exploration, network theory-based recommendations, and machine learning-based recommendations.Striving to operate in increasingly dynamic environments, organisations can be seen as fluid and communicative entities where traditional boundaries fade away and collaborations emerge ad hoc. To enhance fluidity, we conceptualise computational social matching as a research area investigating how to digitally support the development of mutually suitable compositions of collaborative ties in organisations. In practice, it refers to the use of data analytics and digital methods to identify features of individuals and the structures of existing social networks and to offer automated recommendations for matching actors. In this chapter, we outline an interdisciplinary theoretical space that provides perspectives on how interaction can be practically enhanced by computational social matching, both on the societal and organisational levels. We derive and describe three strategies for professional social matching: social exploration, network theory-based recommendations, and machine learning-based recommendations.Peer reviewe

Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research and practice

As far back as the industrial revolution, great leaps in technical innovation succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision making engendering new opportunities for continued innovation. The impact of AI is significant, with industries ranging from: finance, retail, healthcare, manufacturing, supply chain and logistics all set to be disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: technological, business and management, science and technology, government and public sector. The research offers significant and timely insight to AI technology and its impact on the future of industry and society in general

Serendipity and Diversity in Professional Social Matching : Towards diversity-enhancing recommendation strategies

Professional Social Matching (PSM) is the practice of building and maintaining connections in the context of knowledge work. Various people recommender systems and social matching applications have been designed to facilitate PSM by finding relevant others among numerous options. However, conventional recommendation approaches have been found to support algorithmic and human biases, disrupting knowledge flow and social networking, which is vital for PSM. This dissertation focuses on two central concepts: diversity and serendipity. Diversity refers to the importance of exposing individuals to different perspectives, backgrounds, and experiences to foster productive and creative knowledge work. Serendipity, on the other hand, pertains to the occurrence of unsought yet valuable connections that can lead to unexpected and fortunate encounters. The research questions driving this dissertation revolve around the role of diversity and serendipity in PSM tools and the manifestation of these concepts in recommendation strategies. The research process involved a series of five publications. The first two publications employed online surveys to investigate social serendipity and the processes in making valuable connections in online and offline realms. The third publication entails a literature review with a specific emphasis on the conceptual framework of Big Social Data (BSD), as its comprehension holds significant relevance for the domain of user modeling within recommender systems. The last two publications experimented with diversity-enhancing recommendation strategies and examined the alignment between subjective perceptions and objective measures of recommendation relevance. The findings uncovered diverse insights into the characteristics and antecedents of social serendipity, highlighting the necessity for identifying novel mechanisms to foster serendipity experiences in PSM. The results also revealed consistent and significant differences in subjective perceptions of the proposed diversity-enhancing strategies, thus indicating their preliminary effectiveness. Participants showcased the ability to identify relevant others at all levels of similarity and structural network positions, despite the inherent bias in selection. The research contributions lie in elucidating the proactive and reciprocal sense-making involved in PSM, identifying qualities that foster serendipitous encounters, exploring the potential of Big Social Data, and developing and evaluating recommendation mechanisms that promote diversity in professional social networks

Understanding Matchmakers’ Experiences, Principles and Practices of Assembling Innovation Teams

The team composition of a project team is an essential determinant of the success of innovation projects that aim to produce novel solution ideas. Team assembly is essentially complex and sensitive decision-making, yet little supported by information technology (IT). In order to design appropriate digital tools for team assembly, and team formation more broadly, we call for profoundly understanding the practices and principles of matchmakers who manually assemble teams in specific contexts. This paper reports interviews with 13 expert matchmakers who are regularly assembling multidisciplinary innovation teams in various organizational environments in Finland. Based on qualitative analysis of their experiences, we provide insights into their established practices and principles in team assembly. We conceptualize and describe common tactical approaches on different typical levels of team assembly, including arranging approaches like “key-skills-first”, “generalist-first” and “topic-interest-first”, and balancing approaches like “equally-skilled-teams” and “high-expertise-teams”. The reported empirical insights can help to design IT systems that support team assembly according to different tactics.publishedVersionPeer reviewe