Lavanya Aluri’s research while affiliated with Bradley University and other places

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.

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.