A Guide to Actionable Measurement

Outlines the foundation's common principles, approaches, and taxonomies for measuring outcomes in its Global Health, Global Development, and United States program areas at the strategy, initiative, and grant levels. Includes best practices to aspire to

A Quality Model for Actionable Analytics in Rapid Software Development

Background: Accessing relevant data on the product, process, and usage perspectives of software as well as integrating and analyzing such data is crucial for getting reliable and timely actionable insights aimed at continuously managing software quality in Rapid Software Development (RSD). In this context, several software analytics tools have been developed in recent years. However, there is a lack of explainable software analytics that software practitioners trust. Aims: We aimed at creating a quality model (called Q-Rapids quality model) for actionable analytics in RSD, implementing it, and evaluating its understandability and relevance. Method: We performed workshops at four companies in order to determine relevant metrics as well as product and process factors. We also elicited how these metrics and factors are used and interpreted by practitioners when making decisions in RSD. We specified the Q-Rapids quality model by comparing and integrating the results of the four workshops. Then we implemented the Q-Rapids tool to support the usage of the Q-Rapids quality model as well as the gathering, integration, and analysis of the required data. Afterwards we installed the Q-Rapids tool in the four companies and performed semi-structured interviews with eight product owners to evaluate the understandability and relevance of the Q-Rapids quality model. Results: The participants of the evaluation perceived the metrics as well as the product and process factors of the Q-Rapids quality model as understandable. Also, they considered the Q-Rapids quality model relevant for identifying product and process deficiencies (e.g., blocking code situations). Conclusions: By means of heterogeneous data sources, the Q-Rapids quality model enables detecting problems that take more time to find manually and adds transparency among the perspectives of system, process, and usage.Comment: This is an Author's Accepted Manuscript of a paper to be published by IEEE in the 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018. The final authenticated version will be available onlin

Preparing for Climate Impacts: Lessons from the Front Lines

In a synthesis report to The Kresge Foundation, the Georgetown Climate Center shares lessons learned from its adaptation work in recent years. The report includes short case studies highlighting successful efforts as well as barriers to change

Participatory agro-climate information services: A key component in climate resilient agriculture

The brief promotes participatory agro-climate information services as a key component in achieving climate-smart agriculture. The brief emphasizes that actionable agro-climate information starts with—and responds to—gender-based needs of farmers, integrated at all stages of the value chain. Timely forecasts and accurate agroclimate advisories have been proven to provide farmers with production, adaptation, and mitigation benefits

How will the Internet of Things enable Augmented Personalized Health?

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed

Tailoring persuasive health games to gamer type

Persuasive games are an effective approach for motivating health behavior, and recent years have seen an increase in games designed for changing human behaviors or attitudes. However, these games are limited in two major ways: first, they are not based on theories of what motivates healthy behavior change. This makes it difficult to evaluate why a persuasive approach works. Second, most persuasive games treat players as a monolithic group. As an attempt to resolve these weaknesses, we conducted a large-scale survey of 642 gamers' eating habits and their associated determinants of healthy behavior to understand how health behavior relates to gamer type. We developed seven different models of healthy eating behavior for the gamer types identified by BrainHex. We then explored the differences between the models and created two approaches for effective persuasive game design based on our results. The first is a one-size-fits-all approach that will motivate the majority of the population, while not demotivating any players. The second is a personalized approach that will best motivate a particular type of gamer. Finally, to make our approaches actionable in persuasive game design, we map common game mechanics to the determinants of healthy behavior

Predictive User Modeling with Actionable Attributes

Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches

Driving Strategy for Social Impact

Authors Anne Sherman and Paul Connolly offer frameworks and advice to help guide nonprofits and funders through a strategy process. An effective strategy provides leaders with criteria for making important decisions and increasing the overall quality of their work

Framework for Program Assessments

For the past two years, we've been collaborating with our assessment partners to establish a benchmark against which the performance of any student, school or region can be mapped. The scale leverages a nationwide, representative sample of learning competencies prevalent across different school systems. And yet our commitment to evolving and implementing rigorous learning assessment frameworks has never been an end in itself: our primary goal is to use the data and insights to improve learning outcomes.Our work in education assessments—and the benefcial work being done by others in the field—will be most useful if communicated to the broader education community in India. Our assessment partners have prepared reports and conducted workshops to share their findings and explain how our investees can best bring about change in the classroom to enhance academic outcomes

Actionable Supply Chain Management Insights for 2016 and Beyond

The summit World Class Supply Chain 2016: Critical to Prosperity , contributed to addressing a need that the Supply Chain Management (SCM) field’s current discourse has deemed as critical: that need is for more academia-­‐industry collaboration to develop the field’s body of actionable knowledge. Held on May 4th, 2016 in Milton, Ontario, the summit addressed that need in a way that proved to be both effective and distinctive in the Canadian SCM environment. The summit, convened in partnership between Wilfrid Laurier University’s Lazaridis School of Business & Economics and CN Rail, focused on building actionable SCM knowledge to address three core questions: What are the most significant SCM issues to be confronted now and beyond 2016? What SCM practices are imperative now and beyond 2016? What are optimal ways of ensuring that (a) issues of interest to SCM practitioners inform the scholarly activities of research and teaching and (b) the knowledge generated from those scholarly activities reciprocally guide SCM practice? These are important questions for supply chain professionals in their efforts to make sense of today’s business environment that is appropriately viewed as volatile, uncertain, complex, and ambiguous. The structure of the deliberations to address these questions comprised two keynote presentations and three panel discussions, all of which were designed to leverage the collective wisdom that comes from genuine peer-­‐to-­‐peer dialogue between the SCM practitioners and SCM scholars. Specifically, the structure aimed for a balanced blend of industry and academic input and for coverage of the SCM issues of greatest interest to attendees (as determined through a pre-­‐summit survey of attendees). The structure produced impressively wide-­‐ranging deliberations on the aforementioned questions. The essence of the resulting findings from the summit can be distilled into three messages: Given today’s globally significant trends such as changes in population demographics, four highly impactful levers that SCM executives must expertly handle to attain excellence are: collaboration; information; technology; and talent Government policy, especially for infrastructure, is a significant determinant of SCM excellence There is tremendous potential for mutually beneficial industry-academia knowledge co-creation/sharing aimed at research and student training This white paper reports on those findings as well as on the summit’s success in realizing its vision of fostering mutually beneficial industry-academia dialogue. The paper also documents what emerged as matters that are inadequately understood and should therefore be targeted in the ongoing quest for deeper understanding of actionable SCM insights. Deliberations throughout the day on May 4th, 2016 and the encouraging results from the pre-­‐summit and post-­‐summit surveys have provided much inspiration to enthusiastically undertake that quest. The undertaking will be through initiatives that include future research projects as well as next year’s summit–World Class Supply Chain 2017