Global

This presentation featured an SEL Guidebook, which builds on the USAID-commissioned systematic review of SEL. The authors reviewed and researched the emerging evidence for integration of SEL into the school and classroom, including evidence-based approaches that target three categories of SEL: (1) SEL in the classroom and curriculum (i.e. pedagogical interactions that foster SEL and well-being); (2) explicit student-focused activities, and (3) School Climate (what contributes to a context that supports, welcome and nourishes SE development). The findings from this review informed a Guidebook that provides : o Comprehensive set of SEL approaches and activities, with practical examples of each; o Guide for SEL contextualization; o Series “how-to” scenarios for designing and implementing SEL programs based on a context’s needs, culture and policy context. This guidebook serves as a significant contribution to the field in that it identifies the evidence for SEL in LMICs and synthesizes into actionable and digestible information for the busy program designer, donor and/or implementing partner.

This paper brings together two promising intellectual trends in development: Doing Development Differently (DDD), and whole-system reform. In addition, it provides a framework for evaluating system reforms, as rigorously as possible. This paper adapts some concepts from the paper “A Practical Approach to In-Country Systems Research” written for the Research on Improving Systems of Education (RISE) Programme. This version turns more toward the Doing Development Differently movement rather than education systems reform. The original paper was presented at the first RISE conference in Washington, DC, June 18–19, 2015.

“Agent based modeling: A method for understanding individual, social, and environmental influences on learning and equality in the classroom.” Learning science in the past decade has provided considerable evidence that learning is at once emotional, social, and cognitive (MH Immordino-Yang, et al 2018). As we seek to improve social, emotional and learning outcomes in schools around the world, it is important to better understand how individuals adapt to and influence each other and their environments as they connect and interact daily in and outside the school and classroom. Furthermore, it is important to develop a better sense of how local interactions shape education. How do individual interactions shape the patterns of learning outcomes in a school or at a larger scale such as a district? How do they shape the nature of the learning environment and in turn, how do differential learning environments shape the patterns of interactions and relationships in a school? This information holds enormous potential to inform international education programming that may hold promise for improved uptake of innovations and education outcomes. How interactions locally shape education – in schools, administrative offices, or higher education institutions – is not well understood or studied extensively. Agent-based modeling (ABM) is a technique that can be applied to better understand the link between local dynamics of individuals in a school or community and certain aggregate characteristics of a school or emerging school changes. ABMs are based on the application of algorithms or simple rules representing the lower-level interactions of individuals (or system components) that give rise to higher level system structures or changes, providing a tool to understand bottom-up influences on education outcomes. (See M Macy & R Willer, 2002; M Jacobson, et al, 2017). In this presentation we present an agent-based model to show the potential impact of teacher feedback on student participation in the classroom and the relative impact of students who are more or less vulnerable (e.g., have lower/higher ability levels and are from more/less marginalized backgrounds). The model was informed by student data from primary schools in Uganda and Tanzania. The model demonstrates that over time, when met with repeated experiences of negative feedback, more and more students will quit participating entirely and some will dropout, especially children who are more vulnerable. On the other hand, when teachers are increasingly positive, more and more students participate more actively, even among the most vulnerable children. Thus, the nature of teachers’ responses to students when they answer questions in class can powerfully impact student participation and shape equality in participation. To extend, this would seem to impact student learning. The objectives of this presentation are: 1. To introduce the agent-based modeling method. 2. To present an application of ABM in international education 3. To demonstrate the utility of ABM in research, policy dialogue, and programming.

Social network analysis methods for international development: Applications in higher education development. Social Network Analysis (SNA) is a promising yet underutilized tool in the international development field. SNA entails collecting and analyzing data to characterize and visualize social networks, where nodes represent network members and edges connecting nodes represent relationships or exchanges among them. SNA can help both researchers and practitioners understand the social, political, and economic relational dynamics at the heart of international development programming. It can inform program design, monitoring, and evaluation to answer questions related to where people get information; with whom goods and services are exchanged; who people value, trust, or respect; who has power and influence and who is excluded; and how these dynamics change over time. This brief advances the case for use of SNA in international development, outlines general approaches, and discusses two recently conducted case studies in higher education development that illustrate its potential. It concludes with recommendations for how to increase SNA use in international development. Key research recommendations advanced by this paper include: • Incorporate SNA into monitoring, evaluation, and learning processes. SNA can be conducted at various points of a project to inform program design, adaptive management, learning, and evaluation by considering network structure and network changes over time. • Demystify the use of SNA. Increased use of SNA tools and clear presentation in widely read publications are needed to bring the analytic approach into the mainstream of international development. • Build capacity to conduct SNA. The capacity to conduct and interpret SNA is lacking across actors in international development. Efforts by some organizations to build capacity in the community are well noted and should be built upon. • Build understanding of relationships between social networks and development outcomes. SNA will be useful only to the extent it helps users understand the relationship between networks and development outcomes that matter. • Establish norms for data collection and identity protection. Data about individuals and their interactions with others are inherently sensitive data. As a part of standard research ethics protocols, SNA practitioners must make carefully considered decisions about how or if to anonymize data when reporting it.

Title: Right-fit EdTech: Leveraging Loquat and other (machine) learning to support foundational learning in LMICs Abstract The COVID-19 pandemic has severely disrupted global education systems: according to the Global Education Monitoring Team, over 1.6 billion students have lost significant instructional time, with many yet to return to school. But even before the pandemic struck, the state of education around the world was so poor that the World Bank was decrying a “learning crisis.” To address the crisis, the Bank called for both a renewed emphasis on what teachers actually do in a classroom and the strategic deployment of technology to improve teaching and learning. During the pandemic, remote-first instruction became a feature of basic, secondary, and tertiary education systems in many high-income countries. [European Commission, Rickles et al. 2020, Hodges et al. 2020, Gillis & Krull 2020] Coverage was not universal, as wealth and income inequality within high-income countries remains a persistent challenge. [Romm 2020, Blaskó & Schnepf 2020] Nonetheless, the market for educational technology firms boomed during the pandemic, with investment in the sector expected to exceed $80 billion by the end of the decade. [Gillespie 2021] The growth in remote schooling, while temporary, coincides with a long-standing and ongoing interest in so-called “artificial intelligence” or “machine learning” technologies and their applications in business contexts. [Sestino & de Mauro 2021, Benaich & Hogarth 2018, 2019, 2020, 2021] An increasing number of so-called “EdTech” (education technology) companies are riding this wave to attain “unicorn” status (a market valuation in excess of $1 billion), often touting their use of cutting-edge machine learning techniques to support their products or deliver their services. In many lower- and middle-income country (LMIC) contexts, transitions to remote learning were uneven, with significant variance in access, quality, and coverage. [World Bank 2020, UNICEF 2020] Indeed, in many LMICs even the most basic of modern technologies can be unavailable: as of 2015, up to 90 million children in Sub-Saharan Africa were studying in classrooms that lacked electricity. What is the relevance to these education systems of analytical techniques developed in contexts where computing devices are plentiful? This presentation will answer that question in two ways. First, by introducing Loquat, a novel application from RTI International that leverages automatic speech detection and the increasing ubiquity of low-end smartphones to give teachers accessible, affordable instructional coaching. Using machine learning, Loquat detects and classifies verbal interactions between teachers and students. Automated analyses translate these data into simple but powerful visualizations that, combined with guided reflection, provide tailored, actionable feedback teachers can use to understand their talk management and facilitate its improvement. Second, the presentation will review recent advances in machine learning and so-called “artificial intelligence”, detail their potential relevance to lower- and middle-income country education systems, and discuss how programs that aim to improve foundational learning outcomes could begin to leverage these powerful new tools.

On January 18, 2022, the USAID’s Bureau for Asia collaborated with RTI International to reflect on the journey of early grade reading around the globe. The first presenter, Rosalina J. Villaneza, gave an introduction of national-scale early grade literacy assessments in the Philippines. The second presenter, Pilar Robledo, discussed the advent of USAID early grade reading programs, using the EGR Barometer to explore the impact of these programs. The final presenter, Luis Crouch, reflected on research and experience of early grade reading programs, suggesting the next steps on this journey to improve early grade literacy worldwide. View the recording below.

Este manual es el producto de una colaboración en curso entre una gran comunidad de académicos, profesionales, funcionarios gubernamentales y profesionales del desarrollo educativo, para promover la causa de una temprana evaluación y adquisición de la lectura entre los niños de la escuela primaria en países de bajos ingresos.

Led by the Basic Education Coalition (BEC), the Governments' Organizational Responses to COVID-19 - Igniting Interest and Institutional Capacity in EdTech study sought to understand what governments, specifically Ministries of Education (MoE), had to do organizationally to implement these programs, such as forging new partnerships, organizing and capacity building of personnel, developing or revising distance learning policies, and so on. The information shared in this report stems from a global survey implemented across 12 countries in Asia, Africa, the Caribbean and South America with 23 responses from key MoE staff, including several department directors. In doing so, the study fills a knowledge gap for the international basic education sector and provides valuable insights to inform future education systems' capacity building programming. Insights and learnings surfaced from this study provide critical data on MoE capacity, technology investment, and other emerging structural shifts and strategies to support large-scale Education Technology (EdTech) programming and digital transformation of key activities.

This brief describes the use of qualitative analysis in international education research.