This toolkit is the product of ongoing collaboration among a large community of scholars, practitioners, government officials, and education development professionals to advance the cause of early reading assessment and acquisition among primary school children in low-income countries. Extensive peer review comments for this toolkit and suggestions for instrument development were provided by Marcia Davidson, Sandra Hollingsworth, Sylvia Linan-Thompson, and Liliane Sprenger-Charolles. Development of EGRA would not have been possible without the support of the nongovernmental organization and Ministry of Education EGRA Evaluation Teams of Afghanistan, Bangladesh, Egypt, The Gambia, Guyana, Haiti, Honduras, Jamaica, Kenya, Liberia, Mali, Nicaragua, Niger, Peru, Senegal, and South Africa. Our deepest gratitude goes to the teachers, the students, and their families for their participation and continued faith in the benefits of education. In repayment we will diligently seek to improve reading outcomes for all children around the world. Amber Gove is responsible for primary authorship of the toolkit, with contributions from Luis Crouch, Amy Mulcahy-Dunn, and Marguerite Clarke; editing support was provided by Erin Newton. The opinions expressed in this document are those of the authors and do not necessarily reflect the views of the United States Agency for International Development or the World Bank. Please direct questions or comments to Amber Gove at agove@rti.org.

The Improving Learning Outcomes for Asia (ILOA) regional project works closely with USAID’s Asia Bureau and Missions across the region to identify key questions and challenges Mission staff face in their day-to-day work. An advisory group of USAID Mission colleagues raised an important question: how best to wade through the array of education data available? What are the types, how are they used, when are different data useful, etc.? Indeed, the use of different data to effectively partner with governments to make evidence-based decisions is a top priority. As a result, ILOA produced a brief summarizing the different sources and uses of data for basic education, youth and workforce development, and higher education. The brief recognizes how data sources and uses have evolved over time, enabling ministries, their partners, and stakeholders to measure performance, inform policy and plan interventions, and manage limited resources. The brief is designed to succinctly assist USAID staff and their partners in navigating the world of education data.

Community-based methodological support, more accessible information on teacher professional development, and an app that builds students’ reading skills are helping improve learning outcomes in Tajikistan. Over the last 10 years, USAID has supported the Government of Tajikistan to improve the teaching of reading and math in the early grades of primary school. USAID has helped reinforce key features of how the education system supports classroom instruction. Three initiatives described below are particularly notable as lasting contributions to Ministry of Education and Science (MoES) capacity to continue to improve how teachers teach and how students learn to read.

The small, landlocked mountainous nation of Kyrgyzstan occupies an important space in Post Soviet Central Asia – as the only parliamentary democracy in the region since independence in 1992. While the country has admirably maintained near-universal enrollment rates in primary and lower-secondary levels, these important gains in educational access have not been accompanied by adequate learning outcomes. As evidenced by the 2017 National Sample Based Student Assessment, about 60% of grade 4 students in Kyrgyzstan lagged in age-appropriate comprehension level. By all estimates, these learning gaps have worsened due to school closures and economic disruptions caused by COVID-19. While improvements are necessary in many aspects of Kyrgyz school education, few issues are as pressing or as consequential as strengthening the system that prepares and supports the 75,000 public school teachers in the country. In this paper we present innovative models of teacher support structures that hold promise for creating an enabling environment for public school teachers in Kyrgyzstan to grow and succeed in their profession. Specifically, the paper will present insights from two complementary on-going initiatives (each led by one of the co-presenters) that focus on structured observation, feedback, and mentoring mechanisms, and creatively use simple technology applications to promote instructional quality in the classrooms and a community of practice across the system. Our paper will situate the scope of these initiatives in the ecosystem of teacher development practices in Kyrgyzstan and discuss their broader policy applicability. We submit that these insights would be relevant for other resource-constrained education contexts that are aspiring to improve support systems for teachers. The first initiative in focus is the technology-enhanced mentoring model of the Master of Arts in Teaching (MAT) program of the Institute of Education (IOE) at the American University of Central Asia. Launched in 2018, the program – open to both aspiring and in-service teachers – embeds digital pathways in its structure, curricular content, and delivery processes. At the core of the program is a web-video based mentoring model that assigns experienced teachers as mentors for the MAT candidates (mentees). Both mentors and mentees use a lesson observation rubric and simple digital tools (YouTube, Google Form, Google Classroom, Zoom, etc.) to observe, analyze, and reflect on classroom instruction videos, all under the watchful guidance of a dedicated Faculty Advisor from the MAT program. The teaching observation rubric used is a modified version of the evidence-based Danielson Framework for Teaching. Besides providing constructive feedback to the mentees, the mentors are encouraged to model good practice for their mentees and help them identify possible areas of focus and improvement in the subsequent lessons. In other words, these non-hierarchical dialogs are meant to be both evaluative and generative, specific, yet holistic – attentive to mentees’ relative strengths and weaknesses in the context of the specific classroom where they need to perform. Evidence from the assessments by mentors over four cohorts of MAT practicum indicates that thanks to the video-based observation-reflection-feedback loop, the mentees are able to take ownership of their own growth and demonstrate qualitative improvements in their classroom instruction by the end of the practicum. Internal program evaluation data also suggest that the mentors themselves are appreciating benefits of their engagement in the IOE model. Additionally, having dedicated Faculty Advisors overseeing the mentoring program has not only created a support structure for the mentors, but the entire program has also resulted in a broader community of practice. While these are promising results, the scope and scale of a university-based selective program is limited when compared to the needs of the broader education system. This is where the second initiative of this paper - Okuu Keremet! (Learning is Awesome! in Kyrgyz language) is particularly significant. The ongoing USAID funded Okuu Keremet project (2019 – 2024) is designed to help improve learning outcomes in reading and mathematics of more than 450,000 students in Grades 1‒4 in 1,682 target schools in Kyrgyzstan. The project is implemented by RTI (Research Triangle International) in partnership with the Ministry of Education and Sciences of Kyrgyzstan. To date, around 15,000 teachers have completed special pedagogical training in teaching read and math in the primary grades. An important way the project has integrated technology in the improvement of instructional practice is by creation of a Coaching app that is contextualized for easy access and usage by Kyrgyz school teachers and teacher educators. This app assists methodologists to mentor teachers through classroom observations. The program uses a classroom observation rubric / checklist that is easy to interpret, and to update, using the app interface and based on country’s teacher professional standards. Around 3,500 school administrative staff and methodologists of district education departments were trained to mentor teachers in primary schools. The app is being used in 1,682 target schools. Both the IOE model and the Okuu Keremet project underscore the significance of technology-enhanced mentoring in improving instructional practices of classroom teachers in Kyrgyzstan. Data from both initiatives will be presented at the CIES Conference. As leaders of these respective initiatives, we recognize that the promise of our approaches derives from leveraging the power of digital technologies in learning-rich professional development processes for current and aspiring teacher in ways that are evidence-based, context-informed, cost-effective, sustainable, and scalable. Ongoing implementation and refinement of our respective initiatives have uncovered strong levers and weak links in the broader teacher development structures of Central Asia. One critical area is the importance of framing mentoring as a holistic approach to teacher development that goes beyond benchmarking against a rubric and attends to the intersecting concerns of teachers by promoting an ethos of growth mindset and social-emotional support. We submit that developing such holistic mentoring skills and attitudes among skilled and experienced teachers is a policy priority that must be attended to by the Ministry of Education of Kyrgyzstan and its development partners.

Education being recognized as a fundamental right plays a vital role in fostering better societies and ensuring fair access to quality education. In this panel, we will explore the significance of education protests concerning pedagogy, curriculum, and theories. The Philippines Department of Education (DepEd), with support from USAIDs project Advancing Basic education in the Philippines (ABC+), has taken innovative steps to improve learning outcomes and reach marginalized communicates beyond traditional methods. The 2018 Program for International Student Assessment (PISA) revealed that Filipino 15-year-old students scored low in reading comprehension and ranked near the bottom in math and science among 79 countries. This raised concern about curriculum, teaching practice, the learning environments of Philippines schools, and in general the quality of education in the Philippines. It is important to note that over 90% of the students in the Philippines reported speaking a language at home different from the language used in instruction and the PISA test (English). Such language disparity significantly impacts PISA scores, and the Philippines’ linguistic diversity adds to the complexity. The Philippines is one of 44 nations where no single language group exceeds 50% of the total population. Estimates of the number of native Philippine languages range from 110 to 185. The adoption of Mother Tongue Based-Multilingual Education (MTB-MLE) in 2009 recognized this and explicitly emphasized the socio-cultural value of children learning in their maternal languages and put a focus on the importance of language to expanding access to education and improving learning outcomes. This panel highlights the importance of utilizing data to advocate for Early Grade Learning (EGL) and exploring alternative investment pathways beyond traditional sources.

The challenges of conducting educational assessments in low- and middle-income environments during the pandemic can be eased by AI-powered speech recognition technology that offers a promising solution to enhance assessments. By utilizing advanced algorithms and machine learning techniques, this technology accurately transcribes spoken language into written text. Reading fluency and comprehension can be efficiently measured by integrating AI speech recognition into assessments, without the need for physical presence. From the safety of their homes, students can perform the assessments using their smartphones or computers, assisting schools in organizing complex logistics. AI speech recognition technology has a great edge in providing instant feedback, which is one of its main benefits. While students are speaking out loud, the AI system can swiftly assess their intonation, pronunciation, and tempo, rendering quick guidance and identifying areas for refinement. This personalized feedback effortlessly assists students in boosting their reading abilities, even in the absence of in-person teacher interactions. Moreover, AI-backed evaluations can be carried out on a wider scale, enabling educators to collect extensive data on reading patterns and tackle specific issues that are commonly seen among students. The objective of this presentation is to feature the self-administered AI Speech recognition Computer-based reading assessment that RTI developed at the request of the Philippines Department of Education (DepEd), under the USAID All Children Reading (ACR). Throughout the school years of 2020-2022, the COVID-19 pandemic posed significant challenges to conducting face-to-face assessments, particularly in remote learning environments. As a result, teachers faced constraints in terms of time and resources to individually assess learners' reading skills against crucial learning competencies. The proposed automated assessment technology offered a potential solution to alleviate this burden and streamline the evaluation process, allowing educators to efficiently gauge students' reading abilities remotely. In February 2022, ACR-Philippines initiated discussions with USAID and the Philippines Department of Education (DepEd) to produce a ‘proof of concept’ that explores the feasibility of a self-administered computer-based reading assessment (CoBRA) in English and Filipino for students in the Philippines. The concept found resonance with the DepEd leadership as the adoption of a computer-based format for assessments aligns with international practices and provides an excellent opportunity to ascertain students' preparedness to take computer-based tests, such as the Program for International Student Assessment (PISA). The result of this intervention generated a prototype solution piloted and tailored to fit DepED's existing platforms for supporting remote learning and delivery. The pilot provided insights on the feasibility of a computer-based assessment in the context of the Philippines for students in grades 4 -6. The research findings examined the performance, reliability, and results of the AI Speech recognition technology reading assessment, compared to the assessor-administered approach of the assessment. The research generated key design considerations, feedback from end users, recommendations regarding implementing similar approaches, and the future development of similar technology for other languages within and outside the Philippines.

Presentation showcases findings about specific social and emotional skills and their in individual relationships to academic achievement. Importantly, these findings highlight the possible link between inequalities in academic achievement being attributed to the inequalities in SEL. These findings will be published in UNESCO GEMR Spotlight Series 2024.

The presentation summarizes findings from the Rapid Education Risk Analysis for Lebanon

The purpose of this activity was to develop and pilot a CB-EGRA in the Khmer language for validation and use in Cambodia. The CB-EGRA is a paper-based assessment that can be administered with little training to a group of students at one time. It is therefore an arguably more efficient and cost-effective method of obtaining early grade reading data, compared to a traditional EGRA.

Primary Mathematics Skills Assessment Tool: 2021 Stimulus Sheet.