Colleen Heinemann

Building Smart Learning Analytics System for Smart University

The performed analysis of innovative learning analytics systems clearly shows that in the near future those systems will be actively deployed by academic institutions. The on-going research project described here is focused on in-depth analysis of hierarchical levels of learning analytics and academic analytics, types of data to be collected, main features, and the conceptual design of smart learning analytics for smart university. Our vision is that modern analytics systems should strongly support smart university’s “smartness” levels such as adaptivity, sensing, inferring, anticipation, self-learning, and self-organization. This paper presents the up-to-date research outcomes of a research project on the design and development of smart learning analytics systems for smart universities.

Smart Pedagogy for Smart Universities

The performed analysis of innovative technology-based learning and teaching strategies for smart classrooms clearly shows that in the near future smart pedagogy will be actively deployed by leading academic institutions in the world for teaching of local and remote students in one class. The research project is focused on in-depth analysis of innovative learning strategies, including (1) learning-by-doing, (2) flipped classroom, (3) games-based learning, (4) adaptive teaching, (5) context-based learning, (6) collaborative learning, (7) learning analytics, (8) “bring your own device” (BYOD) strategy, (9) personal enquiry based learning, (10) crossover learning, (11) robotics-based learning, and other advanced technology-based approaches to teaching and learning. The obtained outcomes of this performed research, analysis, and testing of implemented smart pedagogy components undoubtedly prove that those learning and teaching strategies support identified “smartness” levels and smart features of smart classrooms such as (1) adaptivity, (2) sensing, (3) inferring, (4) anticipation, (5) self-learning, and (6) self-organization. The obtained student feedback undoubtedly demonstrates students’ strong interest in smart pedagogy – the approach that will be an essential topic of multiple research, design, and development projects in the next 5–10 years.

Multi objective optimization of VPN design by linear programming with risks models

Optimal design of IPsec-based virtual private networks (VPN), in general, depends on multiple factors and parame- ters, such as the VPNs architectural model, hardware and software setups and the technical platform solutions, network topo- logy models, modes of the tunnels operation, levels of the Open Systems Interconnection (OSI) model, encryption/decryption algorithms, modes of cipher operation, security protocols, security associations and key management techniques, connectivity modes, parameters of security algorithms, computer architectures, the number of tunnels in the VPN, and other factors. This paper presents an innovative approach to using methods of linear programming with risks to solve a multi objective optimiza- tion problem of VPN design. In particular, it describes the proposed conceptual VPN design model, VPN information security space, index of effectiveness for multi objective optimization, a new classification of scales, a system-based approach to risks and mathematical modeling of a risk, a hierarchy of scripts and a theorem of description of passwords, VPN design optimization process and particular procedures, sets of legal and illegal VPN design methods, and a VPN design optimization algorithm based on multi objective optimization by linear programming with risks models. The proposed and developed VPN design optimization algorithm was tested by developing specific VPN design methods for various types of VPN users.

Optimal Design of IPsec-Based Mobile Virtual Private Networks for Secure Transfer of Multimedia Data

Optimal design of IPsec-based mobile virtual private networks (MVPN) for a secure transfer of multimedia data, in general case, depends on multiple factors and parameters such as to-be-selected MVPN’s architectural model, hardware and software setups and technical platform’s solutions, network topology models, modes of tunnel’s operation, levels of the Open Systems Interconnection (OSI) model, encryption/decryption algorithms, modes of cipher operation, security protocols, security associations and key management techniques, connectivity modes, parameters of security algorithms, computer architectures, number of tunnels in MVPN, and other factors. This paper presents the outcomes of research project on multi-objective optimization of IPsec mobile virtual private network design based on non-isomorphic groups of order 4—Cayley Tables—for three major MVPN factors: (1) level of data transfer security provided by MVPN, (2) MVPN performance and (3) cost of designed virtual private network.

Algebraic Formalization of Sustainability in Smart University Ranking System

The validity, truthfulness, and reliability of obtained results are of great value when it comes to theory and, specifically, when it comes to risk-free implementations of theoretical research outcomes, deliverables, and findings in practice. The reliability is particularly significant for people who are dependable on outcomes of innovative approaches and developments for human society, such as pedagogy, general theory of education, e-learning, economics, finance, etc. This paper presents the up-to-date outcomes of an on-going research and development project on analysis, design, and engineering of smart educational systems in general and, in particular, theoretical justification of the introduced smart university concept based on the algebraic formalization of smart systems. The obtained theoretical outcomes and findings were successfully validated by their application to an evaluation of sustainability of various universities’ ranking systems.

Smart University: Conceptual Modeling and Systems’ Design

The development of Smart University concepts started just several years ago. Despite obvious progress in this area, the concepts and principles of this new trend are not clarified in full yet. This can be attributed to the obvious novelty of the concept and numerous types of smart systems, technologies and devices available to students, learners, faculty and academic institutions. This paper presents the outcomes of a research project aimed at conceptual modeling of smart universities as a system based on smartness levels of a smart system, smart classrooms, smart faculty, smart pedagogy, smart software and hardware systems, smart technology, smart curriculum, smart campus technologies and services, and other distinctive components. The ultimate goal of this ongoing research project is to develop smart university concepts and models, and identify the main distinctive features, components, technologies and systems of a smart university—those that go well beyond features, components and systems used in a traditional university with predominantly face-to-face classes and learning activities. This paper presents the up-to-date outcomes and findings of conceptual modeling of smart university.

Smart University: Literature Review and Creative Analysis

Research, design, and development of smart universities, smart education, smart classrooms, smart learning environments, smart pedagogy, smart learning and academic analytics, and related topics became the main themes of various pioneering international and national events and projects, governmental and corporate initiatives, institutional agendas, and strategic plans. This paper presents the outcomes of an ongoing project aimed at a systematic literature review and creative analysis of professional publications available in those areas. The premise is that the outcomes of the performed systematic creative analysis will enable researchers to identify the most effective and well-thought ideas, approaches, developed software and hardware systems, technical platforms, smart features and smartness levels, and best practices for the next evolutionary generation of a university—a smart university. The presented Smart Maturity Model can be viewed as an evolutionary approach for a traditional university to progress to various levels of maturity of smart university.