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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.

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.

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 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.

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 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.

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.