Learning Analytics: Shaping the Future of Education
Kazan Federal University, Russia
Abstract: Higher education institutions nowadays are focused on developing and transforming the society through training their students, generating research and encouraging both local and global collaboration in varied areas. Data analytics is a tool to increase the efficacy of the processes aimed at these targets. Today colleges and universities are accumulating and collecting data on almost every aspect of academic life – students (performance, accommodation, finance, social activities, etc.), research (publications, awards, grant proposals, etc.), partners, institutional operations. Traditionally, these data have been used for general purposes of processing simple transactions the institution is involved in. However, the development of sophisticated machine-learning and data-mining techniques, capabilities to store and process big data has let the humanity to move into the era when one can predict the future with a reasonable accuracy – ability that is called Predictive Analytics. Applied to the educational background, this tool creates a relatively new area – Learning Analytics, that involves “measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”. The term can be further developed into Predictive Learning Analytics where one considers the ability to predict the future outcomes of learning data. This ability is a new tool that allows stakeholders in academia such as students, researchers, and administrators, to strategize with more efficacies.
However, the process is facing a number of serious challenges. One of them is the problem of balancing learning analytics with institutional analytics (focuses in the improvement of services across the institution in general), other one is the problem of investment in the area in general. Colleges and universities tend to invest more in institutional analytics with around 40% of higher institutions reporting a major investment in institutional analytics, and about the same percentage of those whose investment in this area is considered minor, while about 45% of institutions either do not invest in learning analytics or invest very little, with only 20% setting it as major priority and thus major investment.
Tools and technology are another aspect that affects the efficiency of applying predictive learning analytics. This is closely connected with the investment problem as only around 20% of institutions use the technology that supports analytics. It also largely depends on the fact that most of this technology is considered experimental and thus requires time to become mainstream or universal.
But probably the most challenging are the problems of decision-making culture, policies, ethics, and data efficacy, which demand enormous time and effort to manage. In this talk, we will look into the fruits and challenges the predictive learning analytics is bringing in the context of the complexity of the issue and tremendous value this technology provides.
Biography: Prof. Jamila MUSTAFINА is the Head of Foreign Languages Departmnent in Kazan Federal University, Russia. She received her PhD in 2007 and full professorship in 2012. Her scientific interests cover the sociolinguistic studies particularly protection of minor languages in the globalized world. Special area of her research is interdicsiplinary approach towards improvement of the social spheres of human life using technology and applied computing. Jamila has published over 70 peer-reviewed scientific articles, 4 books, 2 book chapters. She is currently supervising 5 PhD students and 2 post-docs. She is also acting as a deputy editor-in-chief of the peer reviewed scientific journal “Education and Self Development”. Jamila has been awarded a number of research grants nationally and internationally.
Data Science Applications for Independent Living
Liverpool John Moores University, UK
Abstract: The acceleration of technological change, especially the rise of mass computational availability in the late 20th Century, has lead to the emergence of process and task frameworks that leverage and depend upon technological solutions. Subsequently, the use and acceptance of technology on a wide scale has opened up a new and expanding space of possibilities, allowing us to address many problems that were previously considered intractable, including the redefining the roles of many experts. Meanwhile, the long standing challenge of human healthcare has become a natural domain of interest for the incorporation of such advances in technology, since successful solutions in health translate into healthier populations and improve the quality of life of individuals. In general, the drive towards such technology rich applications, for the purpose of exploring new solution spaces, has been accompanied by an increasing rise in the need to effectively synthesis and manipulate information problems. The healthcare domain is no exception to this phenomenon.
In response, the emergence of a relatively new field called Data Science has resulted, paralleled by the rise in prominence of the Intelligent Systems paradigm. Additionally, the term ‘Big Data’ has been introduced in recognition of the extended use and scale of data, which now features prominently as a critical component in many solutions. The Data Science discipline, along with related concepts, aims to address the problem of information processing, providing a capacity for problem analysis that may exceed immediate human cognitive capabilities, while offloading routine tasks to reduce human cognitive labour and its associated limitations. Data can be viewed as the information carrying substrate within this paradigm, a medium upon which the problem domain information is encoded. In effect, computational processes are now being applied two types of information processing that have remained until recently the exclusive concern of human cognition, namely representing the essential information processing of the human brain via non-biological substrates.
The application of big data solutions, enabled by data science approaches and intelligent system technologies, has already delivered transformative impacts in the health domain. Firstly, The expansion in basic science frameworks towards data intensive processes, using connected and collaborative operational models, has enabled advances in health critical areas including genomics, neuroscience, pharmaceutical development, systems biology, bioinformatics, and others. Prominent related ‘Big Science’ projects include the Human Brain Project in Europe, the Blue Brain Project in Switzerland, the Brain Activity Map in the US, and the BRAIN initiative that is also based in the US. Such ambitious large scale projects, which make extensive use of data science approaches, such as data mining, serve to deliver fundamental insights into human biology and brain function, unlocking new therapeutic solutions. Furthermore, big data and intelligent systems approaches have been applied to the growing space of patient medical data to derive new solutions in disease prediction, patient monitoring, diagnosis, prognosis, pre-surgical evaluation, and world disease burden analysis, among other problem domains. Such a space of intelligent solutions opens up new emerging paradigms in healthcare including P4 medicine (Predictive, personalized, preventive, and participatory), enabling an emphasis on wellness as opposed to disease.
In this talk we explore the intersection of data science, big data, and healthcare, providing a background to problem domains, considering the progress so far, assessing the potential of such approaches, and exploring possible future directions.
Biography: Prof. Dhiya Al-Jumeily is the Associate Dean of External Engagement for the Faculty of Engineering and Technology. He has extensive research interests covering a wide variety of interdisciplinary perspectives concerning the theory and practice of Applied Computing in medicine, human biology, and health care. He has published well over 170 peer reviewed scientific publications, 6 books and 5 book chapters, in multidisciplinary research areas including: Technology Enhanced Learning, Applied Artificial Intelligence, Neural Networks, Signal Prediction, Telecommunication Fraud Detection, AI-based clinical decision-making, medical knowledge engineering, Human-Machine Interaction, intelligent medical information systems, wearable and intelligent devices and instruments. But his current research passion is decision support systems for self-management of health and disease.
Dhiya has successfully supervised 16 PhD students’ studies and has been an external examiner to various UK and overseas Universities for undergraduate, postgraduate and research degrees. He has been actively involved as a member of editorial board and review committee for a number peer reviewed international journals, and is on program committee or as a general chair for a number of international conferences.
Dhiya is also a successful entrepreneur. He is the head of enterprise for the faculty of Engineering and Technology. He has been awarded various commercial and research grants, nationally and internationally, over £3M from Overseas Research and Educational Partners, UK through British Council and directly from industry with portfolio of various Knowledge Transfer Programmes between academia and Industry.
Dhiya has extensive leadership experience including the Development and Management of the Professional Doctorate programme in Engineering and Technology for the University, a founder and Chair of the IEEE International Conference Series on Developments in eSystems Engineering DeSE (www.dese.org.uk) since 2007. He has a large number of international contacts and leads or participates in several international committees in his research fields. Dhiya has one patent and coordinated over 10 projects at national and international level.
Dr. Al-Jumeily is a Senior Member of the IEEE and has achieved his Chartered IT Professional status in 2007. He is also a fellow of the UK Higher Education Academy.