A Model to Identify Affordances for Game-Based Sustainability Learning

Sustainability learning requires the assimilation of domain-specific knowledge and the development of mindsets suitable to engage in complex system dynamics to foster sustainable action. There is a need for bespoke educational models and practical tools to foster sustainability learning. Digital games can answer such need, due to their remarkable potential to wholly engage players in sustainability-related contexts and problems entailing complex dynamics, and the advantages of intrinsically motivating game-based learning processes. However, there is evidence suggesting that such potential might be underexploited. To address this, in this paper we present a model for the identification and analysis of game-based sustainability learning affordances. Our model can be used to support the selection of games for educational purposes, or to facilitate the planning and introduction of game-based sustainability learning affordances when designing new games

Causal Reinforcement Learning: A Survey

Reinforcement learning is an essential paradigm for solving sequential decision problems under uncertainty. Despite many remarkable achievements in recent decades, applying reinforcement learning methods in the real world remains challenging. One of the main obstacles is that reinforcement learning agents lack a fundamental understanding of the world and must therefore learn from scratch through numerous trial-and-error interactions. They may also face challenges in providing explanations for their decisions and generalizing the acquired knowledge. Causality, however, offers a notable advantage as it can formalize knowledge in a systematic manner and leverage invariance for effective knowledge transfer. This has led to the emergence of causal reinforcement learning, a subfield of reinforcement learning that seeks to enhance existing algorithms by incorporating causal relationships into the learning process. In this survey, we comprehensively review the literature on causal reinforcement learning. We first introduce the basic concepts of causality and reinforcement learning, and then explain how causality can address core challenges in non-causal reinforcement learning. We categorize and systematically review existing causal reinforcement learning approaches based on their target problems and methodologies. Finally, we outline open issues and future directions in this emerging field.Comment: 48 pages, 10 figure

Embedded Foundations: Advancing Community Change and Empowerment

· Embedded funders are foundations that have made long-term commitments to the communities in which they are located or work. · Foundations have a long history in funding community development, often with few concrete results. · Political conditions, the increasing divide between rich and poor, inaccessibility of education, lack of housing, and continued segregation and racial discrimination are issues that need be addressed concurrently and resources need to be drawn from a variety of sources, particularly the neighborhoods themselves. This complexity has created an impetus for embedded philanthropy. · Embedded funders work participatively with the community and frame evaluations in less theoretical, more actionable ways. · While the future of embedded philanthropy remains to be seen, there is now a group of funders committed to this way of working

Human Performance Contributions to Safety in Commercial Aviation

In the commercial aviation domain, large volumes of data are collected and analyzed on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that contribute to routine successful outcomes, safety management and system design decisions are based on a small sample of non- representative safety data. Analysis of aviation accident data suggests that human error is implicated in up to 80% of accidents, which has been used to justify future visions for aviation in which the roles of human operators are greatly diminished or eliminated in the interest of creating a safer aviation system. However, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant and largely unrecognized risk when making policy decisions about human roles and responsibilities. Opportunities exist to leverage the vast amount of data that has already been collected, or could be easily obtained, to increase our understanding of human contributions to things going right in commercial aviation. The principal focus of this assessment was to identify current gaps and explore methods for identifying human success data generated by the aviation system, from personnel and within the supporting infrastructure

Systems thinking: critical thinking skills for the 1990s and beyond

This pdf article discusses the need for teaching systems thinking and critical thinking skills. Systems thinking and systems dynamics are important for developing effective strategies to close the gap between the interdependent nature of our problems and our ability to understand them. This article calls for a clearer view of the nature of systems thinking and the education system into which it must be transferred. Educational levels: Graduate or professional

An SEA Guide for Identifying Evidence-Based Interventions for School Improvement

The Every Student Succeeds Act (ESSA) is the most recent reauthorization of the Elementary and Secondary Education Act and replaces the No Child Left Behind Act (NCLB). The law focuses on using research evidence to improve teaching and learning and at the same time passes considerable authority from federal to state policymakers. This means that responsibility largely falls on states and localities to effectively make sense of and use research evidence in their decisions around school improvement, teacher preparation, principal recruitment, and family engagement. With support from the Annie E. Casey Foundation, Overdeck Family Foundation, and the William T. Grant Foundation, the Florida Center for Reading Research (FCRR) has developed Guides for Identifying Evidence-Based Interventions for School Improvement

Individualized Scaffolding of Scientific Reasoning Development – Complementing Teachers with an Auto-agent

Building on the success in a previous study in engaging the underserved middle-school population in the practice of science through individualized scaffolding, the current study sought to examine an automated-agent, Astro-world, developed to provide real-time guidance to students in order to increase the scalability of the intervention while maintaining the benefits of the individualized format. Through practices of argument and counterargument in advancing and challenging claims, the agent focused on coordination of multiple variables affecting an outcome, rather than only the foundational and more extensively studied strategy of controlled experimentation, in the context of a scenario in which students had to investigate multiple factors affecting the performance of potential astronauts in a space simulator. The intervention sought to help students see the purpose and value of scientific practices using social science content rather than traditional science topics. In addition to adapting the technology into a regular classroom setting in which the teacher is actively engaged (teacher-involved condition), the study included a second condition to determine if the technology could be used effectively without active teacher involvement (tech-only condition). Delayed far-transfer assessments showed that only students in the teacher-involved condition (but not the tech-only condition) outperformed those in a non-participating control group in recognizing the need for evidence and considering all contributing factors in making predictions. Furthermore, post-hoc analysis showed that these significant differences occurred predominantly among those who mastered the foundational control of variable skills. Possibilities are considered as to why teacher involvement was critical to effectiveness, and implications for classroom practice are addressed

Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence.

Population health researchers from different fields often address similar substantive questions but rely on different study designs, reflecting their home disciplines. This is especially true in studies involving causal inference, for which semantic and substantive differences inhibit interdisciplinary dialogue and collaboration. In this paper, we group nonrandomized study designs into two categories: those that use confounder-control (such as regression adjustment or propensity score matching) and those that rely on an instrument (such as instrumental variables, regression discontinuity, or differences-in-differences approaches). Using the Shadish, Cook, and Campbell framework for evaluating threats to validity, we contrast the assumptions, strengths, and limitations of these two approaches and illustrate differences with examples from the literature on education and health. Across disciplines, all methods to test a hypothesized causal relationship involve unverifiable assumptions, and rarely is there clear justification for exclusive reliance on one method. Each method entails trade-offs between statistical power, internal validity, measurement quality, and generalizability. The choice between confounder-control and instrument-based methods should be guided by these tradeoffs and consideration of the most important limitations of previous work in the area. Our goals are to foster common understanding of the methods available for causal inference in population health research and the tradeoffs between them; to encourage researchers to objectively evaluate what can be learned from methods outside one's home discipline; and to facilitate the selection of methods that best answer the investigator's scientific questions

Aligning systems science and community-based participatory research: A case example of the Community Health Advocacy and Research Alliance (CHARA).

Partnered research may help bridge the gap between research and practice. Community-based participatory research (CBPR) supports collaboration between scientific researchers and community members that is designed to improve capacity, enhance trust, and address health disparities. Systems science aims to understand the complex ways human-ecological coupled systems interact and apply knowledge to management practices. Although CBPR and systems science display complementary principles, only a few articles describe synergies between these 2 approaches. In this article, we explore opportunities to utilize concepts from systems science to understand the development, evolution, and sustainability of 1 CBPR partnership: The Community Health Advocacy and Research Alliance (CHARA). Systems science tools may help CHARA and other CBPR partnerships sustain their core identities while co-evolving in conjunction with individual members, community priorities, and a changing healthcare landscape. Our goal is to highlight CHARA as a case for applying the complementary approaches of CBPR and systems science to (1) improve academic/community partnership functioning and sustainability, (2) ensure that research addresses the priorities and needs of end users, and (3) support more timely application of scientific discoveries into routine practice