Integrating Big Data Into the Monitoring and Evaluation of Development Programmes

This report provides guidelines for evaluators, evaluation and programme managers, policy makers and funding agencies on how to take advantage of the rapidly emerging field of big data in the design and implementation of systems for monitoring and evaluating development programmes. The report is organized into two parts. Part I: Development evaluation in the age of big data reviews the data revolution and discusses the promise, and challenges this offers for strengthening development monitoring and evaluation. Part II: Guidelines for integrating big data into the monitoring and evaluation frameworks of development programmes focuses on what a big data inclusive M&E system would look like. The report also includes guidelines for integrating big data into programme monitoring and evaluation

Evidence of Innovative Assessment: Literature Review and Case Studies

This report presents the outcomes and analyses of the study Evidence of Innovative Assessment. It provides an overview of innovative (digital and non-digital) assessment approaches and evidence on how these have been implemented to various settings. The first part describes the rational of the study, defines innovative assessment and gathers evidence on the effectiveness of a variety of assessment practices such as self- and peer-assessment, open badges, simulation and learning analytics. The second part presents eight case studies that have integrated innovative assessment approaches from a range of different contexts (formal, non-formal learning, employment, elderly care), covering different age groups, assessment purposes and implementation strategies. Through cross comparisons, the report identifies the challenges and success factors and the replicability of these cases. The report ends with recommendations for research, educational policy and practice.JRC.B.4-Human Capital and Employmen

Evidence of Innovative Assessment : Literature Review and Case Studies

Non peer reviewe

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

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

Sustainable Development Report 2021

The Sustainable Development Report 2021 features the SDG Index and Dashboards, the first and widely used tool to assess country performance on the UN Agenda 2030 and the Sustainable Development Goals. It contains insights on sustainable development and the impact of COVID-19 on the SDGs. This title is available as Open Access on Cambridge Core