Jeffrey P. Bakken

The Ontology of Next Generation Smart Classrooms

Fast proliferation of various types of smart devices, smart systems, and smart technologies provides academic institutions, students and learners with enormous opportunities in terms of new approaches to learning technologies, education, learning processes and strategies, corporate training, user’s personal productivity and efficiency, and faster and better quality of services provided. This paper presents the developed ontology of Smart Classroom systems - it helps to understand and analyze current smart classroom systems, and identify features, hardware, software, services, pedagogy, teaching and learning-related activities of the next generation Smart Classroom systems.

Smart University Taxonomy: Features, Components, Systems

Smart education creates unique and unprecedented opportunities for academic and training organizations in terms of higher standards and innovative approaches to (1) learning and teaching strategies—smart pedagogy, (2) unique highly technological services to local on-campus and remote/online students, (3) set-ups of innovative smart classrooms with easy local/remote student-to-faculty interaction and local/remote student-to-student collaboration, (4) design and development of Web-based rich multimedia learning content with interactive presentations, video lectures, Web-based interactive quizzes and tests, and instant knowledge assessment. This paper presents the outcomes of an ongoing research project aimed to create smart university taxonomy and identify main features, components, technologies and systems of smart universities that go well beyond those in a traditional university with predominantly face-to-face classes and learning activities.

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.

Smart Universities, Smart Classrooms and Students with Disabilities

To better educate in-classroom and remote students we will need to approach education and how we teach various types of students differently. In addition, students these days are more technological than ever and are demanding new and innovative ways to learn. One of the most promising approaches is based on design and development of smart universities and smart classrooms. This paper presents the up-to-date outcomes of research project that is aimed on analysis of students with disabilities and how they might benefit from smart software and hardware systems, and smart technology.

Smart University: Software Systems for Students with Disabilities

Smart universities, smart classrooms and smart education are the wave of the future in a highly technological society. One of the distinctive features of a smart university is its ability of adaptation to and smooth accommodation of various types of students/learners such as regular students and life-long learners, in-classroom/local and remote/online students/learners, regular students and special students, i.e. students with various types of disabilities including physical, visual, hearing, speech, cognitive and other types of impairments. This chapter presents the outcomes of an ongoing research project aimed at systematic identification, analysis, and testing of available open source and commercial text-to-voice, voice-to-text and gesture recognition software systems—those that could significantly benefit students with disabilities. Based on obtained outcomes of completed research and analysis of designated systems we identified and recommended top text-to-voice, voice-to-text and gesture recognition software systems for implementation in smart universities.

Learning Analytics Based Smart Pedagogy: Student Feedback

Learning Analytics is a dynamic interdisciplinary field that encompasses educational sciences and state-of-the-art technology, methods and systems from various fields of computing such as data science, data visualization, software engineering, human-computer interaction, statistics, artificial intelligence, with various stakeholders, e.g. instructors, students, department and college administrators, practitioners, university top managers, computer scientists, IT experts, and software developers. Despite some current achievements and initial developments in Learning Analytics, we are still in a very early stage of development of sophisticated technologies and well-thought practices, tools and applications in this field as well as understanding the impact of Learning Analytics on (a) student learning and privacy, and (b) faculty instruction and autonomy. This paper presents the up-to-date research findings and outcomes of a multi-aspect project on Smart Learning Analytics at Bradley University (USA). It describes the obtained research outcomes about student perception and attitude to Learning Analytics on an academic course level and corresponding Learning Analytics-based pedagogy.

The Quality of Text-to-Voice and Voice-to-Text Software Systems for Smart Universities: Perceptions of College Students with Disabilities

Smart Universities and Smart Classrooms are the wave of the future. To better educate local and distant college students we will need to approach education and how we teach these students differently. In addition, college students are more technological than ever before and are demanding new and innovative ways to learn. This paper presents some ideas about how college students with disabilities might also benefit from Smart Classrooms and smart systems – especially from software systems. Even though students with disabilities are not the majority of learners in our classes, by incorporating university-wide smart systems and technologies we believe many of these students will also benefit. This paper presents the outcomes of a pilot research study analyzing two different commercially available and open source text-to-voice software systems and two different voice-to-text software systems by actual college students with disabilities. It describes (1) testing data obtained from actual college students with disabilities analyzing text-to-voice and voice-to-text software systems, (2) student suggestions for these types of systems for Smart Universities to consider, and (3) the impact these software systems could have on the learning of students with disabilities and how this software could aid universities to a possible transformation from a traditional university into a smart one.

Smart Learning Analytics: Conceptual Modeling and Agile Engineering

Learning analytics focuses on collecting, cleaning, processing, visualization and analyzing teaching and learning related data or metrics from a variety of academic sources. Our vision for engineering of smart learning analytics – the next generation of systems and tools for learning analytics - is based on the concept that this technology should strongly support “smartness” levels of smart academic institutions such as adaptivity, sensing, inferring, anticipation, self-learning, and self-organization. This paper presents the up-to-date findings and outcomes of research, design and development project at the InterLabs Research Institute at Bradley University (Peoria, IL, U.S.A.) that is focused on conceptual modeling of smart learning analytics systems, including identification of goals, objectives, features and functions, main components, inputs and outputs, hierarchical and smartness levels, mathematical methods and algorithms for those systems. Agile software engineering approach has been used for a development of a series of software prototypes to verify the design and development process and validate the obtained outcomes for smart learning analytics systems.

Innovations in Smart Universities

This chapter provides a brief overview of current innovative research in Smart Universities area, including projects on smart technologies, software/hardware systems for Smart Classrooms, and Smart Pedagogy.