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

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyWhile 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

Air Transport Performance and Global Decision Analysis

Since the beginning of aviation, airports have played a pivotal role in Aeronautical Engineering. The airport concept has changed a lot over the past century from small airfields to international hubs. These airport infrastructures have played a significant role in the economic development of the regions they operate. The emergence of the airport city concept as a new successful organisational model suggests that any infrastructure of this kind to be competitive should adopt it. With all its inputs and outputs, the airport industry significantly influences the global economy. The balance between the public interest in general, shareholders, and airport operators must seek to be reconciled. I was investigated how it would be possible to determine whether an airport would have the expected impact on the economy at different scales. Those scales could be that of a continent, a country, a region, or even a city and establish the decision criteria for building (or not) new airport infrastructures and making improvements (or not) in them. Searching for tools that would allow an appropriate evaluation of the management processes of an airport, the measurement of the position of the airport compared to its counterparts (benchmarking) is essential. However, the complexity of the models used makes this tool unfriendly for airport administration. Apart from that, the essential focus of this type of study is the land side of the airport. Nevertheless, there are other types of studies for evaluating the performance of airport processes. Still, these are also complex and do not involve all operational, financial and agent components near the airport. The studies review reinforces the idea that a global analytical tool is essential to find the global perspective (airside, landside, and agents) of any airport's performance beyond the challenges that will be put to them soon and a complete benchmark of direct competitors. The construction of a new methodology requires that airport, land, and airside infrastructures be considered, and agents near the airport, customers, shareholders and airport operators. Thus, a well-founded analysis for a Global Decision Analysis (GDA) incorporates all the infrastructure stakeholders' interconnections in a single tool. GDA is, therefore, friendlier to stakeholders given the management and optimization of decisions based on an analysis system based on the MACBETH multi-criteria methodology, the PESA-AGB. This tool was built and applied to an airport with dimensions identical to Lisbon airport, demonstrating and comparing the evolution of performance and efficiency over 11 years from 2003 to 2013 by six key performance areas of the airport and the respective key performance indicators. The development of an airport efficiency tag for each year of assessment was implemented. An APE-Label implementation, applied to any airport, is presented, and discussed in this study regardless of its size and location. The main obstacle to implementing this APE-Label is the heterogeneity of the airport infrastructure since it differs in the number of runways for public, private or even public-private property, among others. However, with the PESA-AGB methodology, it was possible to mitigate this factor. The main proposal is to provide a graphical APE-Label that informs all interested parties which infrastructure assessment is analysed across the six key performance areas each year that will help to maximize performance and efficiency standards. For the airport case study, a self-benchmarking analysis was carried out for the airport's study with distinctive characteristics representing the central Portuguese air infrastructure. The airport in study is considered the largest in terms of passengers, movements and cargo and is associated with Lisbon airport. Finally, the results of PESA-AGB and GDA have been presented in two visual analysis panels. The dashboards and the GDA report and recommendation are prepared.Desde o início da aviação que os aeroportos detêm um papel fulcral na Engenharia Aeronáutica. O conceito de aeroporto modificou-se muito durante o último século passando de pequenos aeródromos para “hubs” internacionais. Assim estas infraestruturas aeroportuárias têm vindo a assumir um papel muito importante no que diz respeito ao desenvolvimento económico das regiões em que se inserem. O surgimento do conceito de cidade aeroporto, como um novo modelo organizacional de sucesso, sugere que para qualquer infraestrutura do género ser competitiva o deve adotar. A indústria aeroportuária, com todos os seus “inputs” e “outputs”, tem uma grande influência na economia global e os equilíbrios entre o interesse público em geral, os acionistas em particular, os operadores aeroportuários devem procurar ser conciliados. Esta investigação procurou determinar se um aeroporto teria o impacto esperado na economia em diferentes escalas, nomeadamente à de um continente, um país, uma região ou mesmo uma cidade, para poder estabelecer quais os critérios de decisão para a construção de novas infraestruturas aeroportuárias assim como para efetuar melhorias nas mesmas. Na procura de ferramentas que permitissem uma avaliação apropriada dos processos de gestão de um aeroporto, a aferição da posição do aeroporto comparativamente aos seus congéneres (benchmarking) surgiu como essencial. No entanto, a complexidade dos modelos conhecidos em utilização torna as ferramentas pouco amigáveis para a administração aeroportuária, para além de que, o foco essencial deste tipo de estudos é o lado terra do aeroporto. Existem outros tipos de estudos para a avaliação do desempenho dos processos aeroportuários, mas, também estes são complexos e não envolvem todas as componentes operacionais, financeiras e dos agentes na vizinhança do aeroporto. A construção de uma nova metodologia impõe que se tenha em consideração as infraestruturas aeroportuárias, lado terra e lado ar, os clientes, os acionistas e os operadores aeroportuários. Assim, uma análise bem fundamentada para uma decisão a nível global - o Global Decision Analysis (GDA), incorpora numa só ferramenta todas as interligações entre todos os intervenientes da infraestrutura. O GDA é, pois, mais amigável para os stakeholders tendo em vista a gestão e otimização das decisões baseado em um sistema de análise com base na metodologia multicritério MACBETH - o Performance and Efficiency Support Analysis for Airport Global Benchmarking (PESA-AGB), que foi construído e aplicado a um aeroporto com dimensões idênticas às do aeroporto de Lisboa, demonstrando e comparando a evolução do desempenho e eficiência ao longo de 11 anos pelo período de 2003 a 2013 por 6 áreas chave de desempenho do aeroporto e os respetivos indicadores chave de desempenho. Neste estudo, é apresentada e discutida uma implementação da Etiqueta Airport Performance and Efficiency Label (APE-Label), aplicada a qualquer aeroporto, independentemente de seu tamanho e localização. O principal obstáculo à implementação deste APE-Label é a heterogeneidade da infraestrutura aeroportuária, uma vez que esta difere no número de pistas para a propriedade publica, privada ou mesmo público-privada, entre outros. A principal proposta é fornecer uma APE-Label gráfica que informe a todos as partes interessadas qual é a avaliação da infraestrutura analisada através das seis áreas-chave de desempenho em cada ano que ajudarão a maximizar os padrões de desempenho e eficiência. Para o caso de estudo, foi realizada uma análise de self-benchmarking para o aeroporto 1 com características especificas e um aeroporto internacional com as valências de carga e Low Cost Carriers (LCC), representando a principal infraestrutura aérea portuguesa. O aeroporto 1 é considerado o maior em termos de número de passageiros, movimentos e carga e está associado ao aeroporto de Lisboa. Por fim, são apresentados em dois painéis de análise visual os resultados do PESA-AGB e do GDA

Towards Student Engagement Analytics: Applying Machine Learning to Student Posts in Online Lecture Videos

The use of online learning environments in higher education is becoming ever more prevalent with the inception of MOOCs (Massive Open Online Courses) and the increase in online and flipped courses at universities. Although the online systems used to deliver course content make education more accessible, students often express frustration with the lack of assistance during online lecture videos. Instructors express concern that students are not engaging with the course material in online environments, and rely on affordances within these systems to figure out what students are doing. With many online learning environments storing log data about students usage of these systems, research into learning analytics, the measurement, collection, analysis, and reporting data about learning and their contexts, can help inform instructors about student learning in the online context. This thesis aims to lay the groundwork for learning analytics that provide instructors high-level student engagement data in online learning environments. Recent research has shown that instructors using these systems are concerned about their lack of awareness about student engagement, and educational psychology has shown that engagement is necessary for student success. Specifically, this thesis explores the feasibility of applying machine learning to categorize student posts by their level of engagement. These engagement categories are derived from the ICAP framework, which categorizes overt student behaviors into four tiers of engagement: Interactive, Constructive, Active, and Passive. Contributions include showing what natural language features are most indicative of engagement, exploring whether this machine learning method can be generalized to many courses, and using previous research to develop mockups of what analytics using data from this machine learning method might look like

Automated analysis of free-text comments and dashboard representations in patient experience surveys: a multimethod co-design study

BACKGROUND: Patient experience surveys (PESs) often include informative free-text comments, but with no way of systematically, efficiently and usefully analysing and reporting these. The National Cancer Patient Experience Survey (CPES), used to model the approach reported here, generates > 70,000 free-text comments annually. MAIN AIM: To improve the use and usefulness of PES free-text comments in driving health service changes that improve the patient experience. SECONDARY AIMS: (1) To structure CPES free-text comments using rule-based information retrieval (IR) (‘text engineering’), drawing on health-care domain-specific gazetteers of terms, with in-built transferability to other surveys and conditions; (2) to display the results usefully for health-care professionals, in a digital toolkit dashboard display that drills down to the original free text; (3) to explore the usefulness of interdisciplinary mixed stakeholder co-design and consensus-forming approaches in technology development, ensuring that outputs have meaning for all; and (4) to explore the usefulness of Normalisation Process Theory (NPT) in structuring outputs for implementation and sustainability. DESIGN: A scoping review, rapid review and surveys with stakeholders in health care (patients, carers, health-care providers, commissioners, policy-makers and charities) explored clinical dashboard design/patient experience themes. The findings informed the rules for the draft rule-based IR [developed using half of the 2013 Wales CPES (WCPES) data set] and prototype toolkit dashboards summarising PES data. These were refined following mixed stakeholder, concept-mapping workshops and interviews, which were structured to enable consensus-forming ‘co-design’ work. IR validation used the second half of the WCPES, with comparison against its manual analysis; transferability was tested using further health-care data sets. A discrete choice experiment (DCE) explored which toolkit features were preferred by health-care professionals, with a simple cost–benefit analysis. Structured walk-throughs with NHS managers in Wessex, London and Leeds explored usability and general implementation into practice. KEY OUTCOMES: A taxonomy of ranked PES themes, a checklist of key features recommended for digital clinical toolkits, rule-based IR validation and transferability scores, usability, and goal-oriented, cost–benefit and marketability results. The secondary outputs were a survey, scoping and rapid review findings, and concordance and discordance between stakeholders and methods. RESULTS: (1) The surveys, rapid review and workshops showed that stakeholders differed in their understandings of the patient experience and priorities for change, but that they reached consensus on a shortlist of 19 themes; six were considered to be core; (2) the scoping review and one survey explored the clinical toolkit design, emphasising that such toolkits should be quick and easy to use, and embedded in workflows; the workshop discussions, the DCE and the walk-throughs confirmed this and foregrounded other features to form the toolkit design checklist; and (3) the rule-based IR, developed using noun and verb phrases and lookup gazetteers, was 86% accurate on the WCPES, but needs modification to improve this and to be accurate with other data sets. The DCE and the walk-through suggest that the toolkit would be well accepted, with a favourable cost–benefit ratio, if implemented into practice with appropriate infrastructure support. LIMITATIONS: Small participant numbers and sampling bias across component studies. The scoping review studies mostly used top-down approaches and focused on professional dashboards. The rapid review of themes had limited scope, with no second reviewer. The IR needs further refinement, especially for transferability. New governance restrictions further limit immediate use. CONCLUSIONS: Using a multidisciplinary, mixed stakeholder, use of co-design, proof of concept was shown for an automated display of patient experience free-text comments in a way that could drive health-care improvements in real time. The approach is easily modified for transferable application. FUTURE WORK: Further exploration is needed of implementation into practice, transferable uses and technology development co-design approaches. FUNDING: The National Institute for Health Research Health Services and Delivery Research programme

A framework for strategic planning of data analytics in the educational sector

The field of big data and data analysis is not a new one. Big data systems have been investigated with respect to the volume of the data and how it is stored, the data velocity and how it is subject to change, variety of data to be analysed and data veracity referring to integrity and quality. Higher Education Institutions (HEIs) have a significant range of data sources across their operations and increasingly invest in collecting, analysing and reporting on their data in order to improve their efficiency. Data analytics and Business Intelligence (BI) are two terms that are increasingly popular over the past few years in the relevant literature with emphasis on their impact in the education sector. There is a significant volume of literature discussing the benefits of data analytics in higher education and even more papers discussing specific case studies of institutions resorting on BI by deploying various data analytics practices. Nevertheless, there is a lack of an integrated framework that supports HEIs in using learning analytics both at strategic and operational level. This research study was driven by the need to offer a point of reference for universities wishing to make good use of the plethora of data they can access. Increasingly institutions need to become ‘smart universities’ by supporting their decisions with findings from the analysis of their operations. The Business Intelligence strategies of many universities seems to focus mostly on identifying how to collect data but fail to address the most important issue that is how to analyse the data, what to do with the findings and how to create the means for a scalable use of learning analytics at institutional level. The scope of this research is to investigate the different factors that affect the successful deployment of data analytics in educational contexts focusing both on strategic and operational aspects of academia. The research study attempts to identify those elements necessary for introducing data analytics practices across an institution. The main contribution of the research is a framework that models the data collection, analysis and visualisation in higher education. The specific contribution to the field comes in the form of generic guidelines for strategic planning of HEI data analytics projects, combined with specific guidelines for staff involved in the deployment of data analytics to support certain institutional operations. The research is based on a mixed method approach that combines grounded theory in the form of extensive literature review, state-of-the-art investigation and case study analysis, as well as a combination of qualitative and quantitative data collection. The study commences with an extensive literature review that identifies the key factors affecting the use of learning analytics. Then the research collected more information from an analysis of a wide range of case studies showing how learning analytics are used across HEIs. The primary data collection concluded with a series of focus groups and interviews assessing the role of learning analytics in universities. Next, the research focused on a synthesis of guidelines for using learning analytics both at strategic and operational levels, leading to the production of generic and specific guidelines intended for different university stakeholders. The proposed framework was revised twice to create an integrated point of reference for HEIs that offers support across institutions in scalable and applicable way that can accommodate the varying needs met at different HEIs. The proposed framework was evaluated by the same participants in the earlier focus groups and interviews, providing a qualitative approach in evaluating the contributions made during this research study. The research resulted in the creation of an integrated framework that offers HEIs a reference for setting up a learning analytics strategy, adapting institutional policies and revising operations across faculties and departments. The proposed C.A.V. framework consists of three phases including Collect, Analysis and Visualisation. The framework determines the key features of data sources and resulting dashboards but also a list of functions for the data collection, analysis and visualisation stages. At strategic level, the C.A.V. framework enables institutions to assess their learning analytics maturity, determine the learning analytics stages that they are involved in, identify the different learning analytics themes and use a checklist as a reference point for their learning analytics deployment. Finally, the framework ensures that institutional operations can become more effective by determining how learning analytics provide added value across different operations, while assessing the impact of learning analytics on stakeholders. The framework also supports the adoption of learning analytics processes, the planning of dashboard contents and identifying factors affecting the implementation of learning analytics

Green Cities Artificial Intelligence

119 pagesIn an era defined by rapid urbanization, the effective planning and management of cities have become paramount to ensure sustainable development, efficient resource allocation, and enhanced quality of life for residents. Traditional methods of urban planning and management are grappling with the complexities and challenges presented by modern cities. Enter Artificial Intelligence (AI), a disruptive technology that holds immense potential to revolutionize the way cities are planned, designed, and operated. The primary aim of this report is to provide an in-depth exploration of the multifaceted role that Artificial Intelligence plays in modern city planning and management. Through a comprehensive analysis of key AI applications, case studies, challenges, and ethical considerations, the report aims to provide resources for urban planners, City staff, and elected officials responsible for community planning and development. These include a model City policy, draft informational public meeting format, AI software and applications, implementation actions, AI timeline, glossary, and research references. This report represents the cumulative efforts of many participants and is sponsored by the City of Salem and Sustainable City Year Program. The Green Cities AI project website is at: https://blogs.uoregon.edu/artificialintelligence/. As cities continue to evolve into complex ecosystems, the integration of Artificial Intelligence stands as a pivotal force in shaping their trajectories. Through this report, we aim to provide a comprehensive understanding of how AI is transforming the way cities are planned, operated, and experienced. By analyzing the tools, applications, and ethical considerations, we hope to equip policymakers, urban planners, and stakeholders with the insights needed to navigate the AI-driven urban landscape effectively and create cities that are not only smart but also sustainable, resilient, and regenerative.This year's SCYP partnership is possible in part due to support from U.S. Senators Ron Wyden and Jeff Merkley, as well as former Congressman Peter DeFazio, who secured federal funding for SCYP through Congressionally Directed Spending. With additional funding from the city of Salem, the partnerships will allow UO students and faculty to study and make recommendations on city-identified projects and issues

Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region

This open access book is a consolidation of lessons learnt and experiences gathered from our efforts to utilise Earth observation (EO) science and applications to address environmental challenges in the Hindu Kush Himalayan region. It includes a complete package of knowledge on service life cycles including multi-disciplinary topics and practically tested applications for the HKH. It comprises 19 chapters drawing from a decade’s worth of experience gleaned over the course of our implementation of SERVIR-HKH – a joint initiative of NASA, USAID, and ICIMOD – to build capacity on using EO and geospatial technology for effective decision making in the region. The book highlights SERVIR’s approaches to the design and delivery of information services – in agriculture and food security; land cover and land use change, and ecosystems; water resources and hydro-climatic disasters; and weather and climate services. It also touches upon multidisciplinary topics such as service planning; gender integration; user engagement; capacity building; communication; and monitoring, evaluation, and learning. We hope that this book will be a good reference document for professionals and practitioners working in remote sensing, geographic information systems, regional and spatial sciences, climate change, ecosystems, and environmental analysis. Furthermore, we are hopeful that policymakers, academics, and other informed audiences working in sustainable development and evaluation – beyond the wider SERVIR network and well as within it – will greatly benefit from what we share here on our applications, case studies, and documentation across cross-cutting topics