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 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.

This presentation features the instructional design of a blended learning online course on formative assessment developed at the request of the Philippine Department of Education (DepEd), under the USAID/Philippines All Children Reading task order. In 2020, DepEd, through the National Educators Academy of the Philippines (NEAP), issued a policy aimed at strengthening the quality of TPD. The policy calls for several transformations, including the use of a wider range of learning modalities, such as job-based learning and teacher communities of practice; the adoption of a wider range of delivery platforms, such as online and blended learning; and the immediate application of TPD lessons in classrooms. The adoption of the policy has been slow and there are few exemplars of approved courses that meet these requirements. The challenge was to design a learning experience for teachers that not only exemplified these requirements but was also flexible enough to adapt to the myriad school contexts in the Philippines and was ready to be taken to scale by regional DepEd offices―without donor support―before the closeout of USAID funding for this activity.

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.

Kindergarten Classroom Observation Assessor Sheet.

Jordan EGRA 2021 stimulus sheet.

2021 Primary Reading Skills Assessment Tool, Assessor Instructions Form.

Classroom Observation Tool for Grade 1 to Grade 3 Mathematics.

This presentation focused on learning through play-based pedagogies in Kenya, Rwanda and Ghana. Some key findings discussed are: (1) Teachers in general support the concept of play and employing play-based teaching in the classroom, but far fewer are okay with students directing their own learning. (2) In practice, teachers spend the most time explaining/lecturing and monitoring the class. (3) At midline, significant changes were detected in some aspects of teacher practice in Kenya and Ghana, but overall, the frequency of play-based approaches is low.

This presentation on development of an assessment system under Senegal's new bilingual policy includes an explanation of the bilingual policy in Senegal and plans for development of a national assessment system including use of the ANLAS methodology (Analysis of National Learning Assessment Systems)