Africa’s digital solutions to tackle COVID-19

African countries are using technology in many new ways to fight the coronavirus pandemic. This report highlights some of the best digital solutions and estimates the investments required to implement the technology on a wider scale. The European Investment Bank prepared this report with the support of the United Nations Development Programme and the consulting firm BearingPoint

Africa’s digital solutions to tackle COVID-19

African countries are using technology in many new ways to fight the coronavirus pandemic. This report highlights some of the best digital solutions and estimates the investments required to implement the technology on a wider scale. The European Investment Bank prepared this report with the support of the United Nations Development Programme and the consulting firm BearingPoint

Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning

[EN] Data analysis is a key process to foster knowledge generation in particular domains or fields of study. With a strong informative foundation derived from the analysis of collected data, decision-makers can make strategic choices with the aim of obtaining valuable benefits in their specific areas of action. However, given the steady growth of data volumes, data analysis needs to rely on powerful tools to enable knowledge extraction. Information dashboards offer a software solution to analyze large volumes of data visually to identify patterns and relations and make decisions according to the presented information. But decision-makers may have different goals and, consequently, different necessities regarding their dashboards. Moreover, the variety of data sources, structures, and domains can hamper the design and implementation of these tools. This Ph.D. Thesis tackles the challenge of improving the development process of information dashboards and data visualizations while enhancing their quality and features in terms of personalization, usability, and flexibility, among others. Several research activities have been carried out to support this thesis. First, a systematic literature mapping and review was performed to analyze different methodologies and solutions related to the automatic generation of tailored information dashboards. The outcomes of the review led to the selection of a modeldriven approach in combination with the software product line paradigm to deal with the automatic generation of information dashboards. In this context, a meta-model was developed following a domain engineering approach. This meta-model represents the skeleton of information dashboards and data visualizations through the abstraction of their components and features and has been the backbone of the subsequent generative pipeline of these tools. The meta-model and generative pipeline have been tested through their integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully integrated with other meta-model to support knowledge generation in learning ecosystems, and as a framework to conceptualize and instantiate information dashboards in different domains. In terms of the practical applications, the focus has been put on how to transform the meta-model into an instance adapted to a specific context, and how to finally transform this later model into code, i.e., the final, functional product. These practical scenarios involved the automatic generation of dashboards in the context of a Ph.D. Programme, the application of Artificial Intelligence algorithms in the process, and the development of a graphical instantiation platform that combines the meta-model and the generative pipeline into a visual generation system. Finally, different case studies have been conducted in the employment and employability, health, and education domains. The number of applications of the meta-model in theoretical and practical dimensions and domains is also a result itself. Every outcome associated to this thesis is driven by the dashboard meta-model, which also proves its versatility and flexibility when it comes to conceptualize, generate, and capture knowledge related to dashboards and data visualizations

Faculty Senate Monthly Packet February 2022

The February 7, 2022 Monthly packet includes the February agenda and appendices and the Faculty Senate minutes and attachments from the meeting held January 3, 2022

Specifying information dashboards’ interactive features through meta-model instantiation

[EN]Information dashboards1 can be leveraged to make informed decisions with the goal of improving policies, processes, and results in different contexts. However, the design process of these tools can be convoluted, given the variety of profiles that can be involved in decision-making processes. The educative context is one of the contexts that can benefit from the use of information dashboards, but given the diversity of actors within this area (teachers, managers, students, researchers, etc.), it is necessary to take into account different factors to deliver useful and effective tools. This work describes an approach to generate information dashboards with interactivity capabilities in different contexts through meta-modeling. Having the possibility of specifying interaction patterns within the generative workflow makes the personalization process more fine-grained, allowing to match very specific requirements from the user. An example of application within the context of Learning Analytics is presented to demonstrate the viability of this approach

Development of an on-job mentorship programme to improve nursing experience for enhanced patient experience of compassionate care

Background: Evidence suggests improvement in nursing staff satisfaction, competence, and retention after implementation of evidence-based mentorship programmes. When guided by a framework of compassion, mentoring as a caring action can not only build healthy, transformative relationships but a similar behavior is reciprocated to patients which subsequently can drive patient experience of care. However, examples of on-job mentorship programs for nurses in low- and middle-income countries (LMIC) are limited.Objective: The objective of the study was to develop an on-job nursing mentorship programme using a compassionate framework aimed at improving nurses\u27 experience and thus enhancing patient experience in a tertiary care hospital in Pakistan.Methods: Designed as an intervention development study, it was completed between January 2018-December 2019. The programme was developed by a team composed of service and nursing leadership, director patient experience of care and a compassion specialist using a theory of change model. The package followed a series of steps, a) identification of a framework, b) creation of working group c) needs assessment and d) multiple meetings to frame the model followed by implementing the preconditions for roll-out of the programme with the frontline staff.Results: The eventual outcome was improving the patient\u27s experience of compassion while the intermediate outcome was to have nurses demonstrate compassionate care. The pre-conditions were identified as: recruitment of staff with appropriate skills for pediatric care, provision of compassionate experience to the frontline nurses by addressing their specific pain points, development of competent head nurses as supervisors and creation of a compassionate culture. To ensure the pre-conditions, various interventions were planned with some implemented through the course of the study while others are in the process of being rolled out. These involved, inclusion of pediatric compassion specific module during orientation of new hires, creation of space to talk about compassionate skills with staff, provision of trainings and mentorship to create competent head nurses, and creating a culture that promoted and recognized compassionate care values.Conclusion: The approach helped to delineate feasible pathways for an on-job compassionate mentorship programme enhancing routine supervisors\u27 role as facilitators of compassionate care

Information Dashboards and Tailoring Capabilities: A Systematic Literature Review

[EN]The design and development of information dashboards are not trivial. Several factors must be accounted; from the data to be displayed to the audience that will use the dashboard. However, the increase in popularity of these tools has extended their use in several and very different contexts among very different user pro les. This popularization has increased the necessity of building tailored displays focused on speci c requirements, goals, user roles, situations, domains, etc. Requirements are more sophisticated and varying; thus, dashboards need to match them to enhance knowledge generation and support more complex decision-making processes. This sophistication has led to the proposal of new approaches to address personal requirements and foster individualization regarding dashboards without involving high quantities of resources and long development processes. The goal of this work is to present a systematic review of the literature to analyze and classify the existing dashboard solutions that support tailoring capabilities and the methodologies used to achieve them. The methodology follows the guidelines proposed by Kitchenham and other authors in the eld of software engineering. As results, 23 papers about tailored dashboards were retrieved. Three main approaches were identi ed regarding tailored solutions: customization, personalization, and adaptation. However, there is a wide variety of employed paradigms and features to develop tailored dashboards. The present systematic literature review analyzes challenges and issues regarding the existing solutions. It also identi es new research paths to enhance tailoring capabilities and thus, to improve user experience and insight delivery when it comes to visual analysis

Human resources analytics module at Quidgest: One more step for human resources to become a true strategic partner

Analytics has been a source of competitive advantage due to improved decision-making processes in several business areas. Organizations have reported gains in efficiency and effectiveness based on the implementation of data-driven strategies. However, Human Resources (HR) professionals have been struggling to implement Analytics processes and are missing out on the opportunity of using data to improve organizational performance and truly become a Business Strategic Partner. This Enterprise Project aims to contribute to shortening that gap. It sets out to gather and elicit business, user, functional, and nonfunctional requirements for a new Human Resources Analytics Module (HRAM) at Quidgest, a Portuguese Technological Consultancy company that develops Human Resources Information Systems. The gathering and elicitation of requirements were done through Interviews, a Questionnaire, and 2 Joint Application Development (JAD) Sessions. A Value Proposition Canvas was developed to convey a fit between the system’s main functionalities and HR Professionals’ needs based on those requirements. The relevance of this project is two-folded: First, when developed, the new Analytics Module can become a new revenue stream for Quidgest and a way to maintain and improve its competitiveness in the market; Second, HR Professionals may find a new tool that meets their needs towards implementing Analytics processes and take a step forward in becoming a Strategic Partner. The conclusion of this project also sets out to suggest the next steps for the Module Development and implementation.O uso de Analytics tem sido uma fonte de vantagem competitiva devido à melhoria dos processos de tomada de decisão. As organizações relatam ganhos em eficiência e eficácia com base na implementação de estratégias baseadas em análise de dados. No entanto, os profissionais de Recursos Humanos (RH) têm se debatido para implementar processos analíticos e estão a perder a oportunidade de usar os seus dados para melhorar o desempenho organizacional e se tornarem realmente Strategic Business Partners. Este projeto em empresa visa colmatar essa lacuna. Pretende-se recolher e clarificar requisitos de negócio, utilizador, funcionais e não funcionais para um novo Módulo de Human Resources (HR) Analytics na Quidgest, uma constultora tecnológica portuguesa que desenvolve Sistemas de Informação de RH. A recolha e a clarificação de requisitos foi feita através de entrevistas, um questionário, e 2 Joint Application Development Sessions. De seguida, foi desenvolvido um Value Proposition Canvas, que mostra como há um fit entre as principais funcionalidades do sistema e as necessidades dos profissionais de RH nesta área. A relevância deste projeto prende-se em dois aspetos: primeiro, o novo Módulo de Analytics pode tornar-se uma nova fonte de receita para a Quidgest e uma forma de manter e melhorar sua competitividade; Em segundo lugar, os profissionais de RH podem encontrar uma nova ferramenta que responda às suas necessidades de implementação de processos analíticos e dar um passo em frente para se tornarem um Business Partner. A conclusão deste projeto sugere os próximos passos para o Desenvolvimento do Módulo de Analytics