• Supporting student experience management with learning analytics in the UK higher education sector

      Kika, Claudette Adamma (University of BedfordshireUniversity of Bedfordshire, 2018-08)
      While some UK Higher Education Institutes (HEIs) are very successful at harnessing the benefits of Learning Analytics, many others are not actually engaged in making effective use of it. There is a knowledge gap concerning understanding how Learning Analytics is being used and what the impacts are in UK HEIs. This study addresses this gap. More specifically, this study attempts to understand the challenges in utilising data effectively for student experience management (SEM) in the era of Big Data and Learning Analytics; to examine how Learning Analytics is being used for SEM; to identify the key factors affecting the use and impact of Learning Analytics; and to provide a systematic overview on the use and impact of Learning Analytics on SEM in HEIs by developing a conceptual framework. To achieve the research objectives, a qualitative research method is used. The data collection process firstly involves an exploratory case study in a UK university to gain a preliminary insight into the current status on the use of Big Data and Learning Analytics and their impact, and to determine the main focuses for the main study. The research then undertakes an extensive main study involving 30 semi-structured interviews with participants in different UK universities to develop more in-depth knowledge and to present systematically the key findings using a theoretical framework underpinned by relevant theories. Based on the evidence collected from the exploratory case study and interviews, the study identifies the key challenges in utilising data and Learning Analytics in the era of Big Data. These include issues related to data quality, data consistency, data reliability, data analysis, data integration, data and information overload, lack of data, information availability and problems with systems. A series of critical factors affecting the use of Learning Analytics is emerged and mapped out from a technology-organisation-environment-people (TOE+P) perspective. The technology-related factors include Usability, Affordability, Complexity and System integration. The organisation-related factors cover Resource, Data Driven Culture, Senior management support and Strategic IT alignment. The environment-related factors include Competitive pressure, Regulatory environment and External support. Most importantly, the findings emphasise the importance of the people-related factor in addition to TOE factors. The people-related factors include People’s engagement with using data and Learning Analytics, People’s awareness of Data Protection and Privacy and Digital Literacy. The impacts of the Learning Analytics are also identified and analysed using organisational absorptive capacity theory. The findings are integrated in the final theoretical framework and demonstrate that the HEIs’ capabilities in terms of data acquisition, assimilation, transformation and exploitation supported by Learning Analytics enable them to improve student experience management. This study makes new contributions to research and theory by providing a theoretical framework on understanding the use and impact of Learning Analytics in UK HEIs. It also makes important practical contributions by offering valuable guidelines to HEI managers and policy makers on understanding the value of Learning Analytics and know how to maximise the impact of Big Data and Learning Analytics in their organisations.