The conventional ways that new pedagogies are taught and supported in low- and middle-income countries rarely leverage the social networks and relationships that are paramount for individuals to shift their beliefs and make positive decisions about adopting new methodologies, and for them to sustain these behavior changes in the long run. The purpose of this study was to apply social network analysis (SNA) and qualitative inquiry to understand the composition and structure of primary school teacher social networks to inform policy basic education programming about how to improve the diffusion and support for implementation of new pedagogies through these social networks. This study used a mixed model design to study the compositional and structural properties of teacher social networks in sub-district administrative areas or “wards” in Tanzania. Using data from completed socio-metric inventories, separate teacher social networks were generated for Mbawala, Madimba, Milangominne, and Nitekela wards in the Mtwara Region of Tanzania. Researchers used SNA to calculate quantitative measurements and generate teacher sociograms (i.e., network graphs) for each of the ward-level teacher social networks. We combined these data with data from teacher informant interviews that described the content, context, and benefit of educators’ interactions in the different wards and to help explain the SNA findings.

The purpose of this paper is to present a methodology for understanding materials usage in primary classrooms in Sub-Saharan Africa that centers teachers’ actions and voices. The United Nation’s Sustainable Development Goal 4 focuses on improved primary education around the world. To meet this goal, many large, donor-funded interventions aim to improve education through provision of teacher’s guides and student textbooks. However, what many of these interventions lack is a systematic way to understand how and why teachers are making pedagogical decisions while using materials. There is a large body of work that seeks to understand how teachers make decisions as they teach, and the ways these decisions are influenced by their knowledge and beliefs. Drawing from this work, we describe a methodology and set of tools that uses observations and interviews to identify key decisions that teachers make in the classroom and why teachers made those decisions. We piloted and iteratively refined this methodology over the course of three studies and use examples from these studies to illustrate the methodology. By closely observing and listening to teachers, we gain insights that allow us to continually refine and improve materials to ultimately improve the quality of classroom instruction.

This report summarizes the findings of an effort to develop and validate tablet-based, self-administered assessments of English-language foundational literacy and numeracy in the early grades. The tools described in the report were developed at the request of Imagine Worldwide with the support of the Jacobs Foundation. RTI carried out field testing and a pilot study to assess the tools' internal consistency, test-retest reliability, and concurrent validity with respect to "traditional" EGRA and EGMA. RTI International developed the two assessments, known respectively as the Self-Administered Early Grade Reading Assessment (SA-EGRA) and the Self-Administered Early Grade Mathematics Assessment (SA-EGMA), with the support and at the direction of Imagine Worldwide. The assessments are deemed “self-administered,” because children complete the assessments independently in response to instructions and stimuli imbedded in the tablet-based software. However, adults typically supervise the organization and conduct of the assessment as well as the collection of individual data from the tablets for analysis. The tools have been developed under an open-source license. The code can be viewed and downloaded for reuse or modification at https://github.com/ICTatRTI/SE-tools/blob/main/README.md. Users of RTI's Tangerine software may request that the SA-EGRA and SA-EGMA tools be added to their Tangerine groups via https://www.tangerinecentral.org/contact

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.

The purpose of this session is to articulate a research framework that centers teachers’ voices when trying to understand how teachers use curriculum materials in the classroom. Operating in the context of highly structured lesson plans, the approach identifies ways in which teachers exercise their professional discretion to modify the lesson and frames conversations to elaborate the motivations driving the teachers’ choices. The approach has been iteratively refined across three studies; taken together, the studies provide evidence for the value of listening to teachers and being responsive to their voices during implementation. The research framework uses the lens of modifications, or changes to the intended lesson plan implemented within one class period. Modifications can be large or small, additive or subtractive. For example, in a lesson with a section for independent student practice, a large modification might be skipping the practice section entirely. Or, during a lesson focused on blending of initial sounds using 3 example words, a small modification might be extending the exercise by adding extra words. Researchers observe the lesson, noting any modifications; after the lesson, the researchers select some of the modifications and ask the teachers why they made the choices they did. Analyses of teachers’ explanations highlight the importance of understanding why teachers make the choices they do. For example, a teacher who skipped the independent practice section because they don’t think their students are ready to do the skill on their own suggests that the teacher is exercising agency and using her knowledge of her students to inform her decision-making. Insights such as this one can guide decisions on projects. It may be that while the intention of the teacher was guided by knowledge of students, the end result is not desirable from the project’s point of view. Understanding why the teacher made this choice provides implementers with better and targeted ways to address choices that impact the overall goals of the project. In this presentation, we draw on data from an exploratory case study to understand use of new mathematics materials in Liberia, a more in-depth case conducted in Malawi on teacher use of reading materials, and finally, a systematic study examining how reading teachers use materials across four Sub-Saharan African countries. We use each case to highlight both an aspect of the research framework and instances of modifications to project implementation driven by teachers’ voices. By focusing on teacher voices, we disrupt the deficit notion that teachers are “resistant” to change, or do not “understand” new pedagogies. Instead, we aim to value teacher voices and integrate their insight into implementation programs. By doing this, we not only raise the likelihood of successful use of new materials and pedagogies, but we also develop more responsive pedagogy that better matches existing classroom cultures.

Chapter 5 of the book Cultivating Dynamic Educators: Case Studies in Teacher Behavior Change in Africa and Asia. This chapter critically reviews the design, implementation, and evaluation of an attempt to study an exploratory short message service (SMS)–based intervention conducted under the auspices of the United States Agency for International Development’s (USAID’s) Malawi Early Grade Reading Activity (EGRA).1 The overall EGRA program, which was implemented from July 2013 to October 2016 in 1,614 schools across 11 educational districts, was designed to support Malawi’s Ministry of Education, Science, and Technology (MOEST) to improve reading outcomes in both Chichewa. and English languages among children in grades 1–3. The

LARA developed action research tools to measure the level of fidelity of implementation of the EGR methods and generate lessons learned to inform adaptations in EGR programming. The action research tools gather both historical and real-time data at the school. They include the EGR core methodologies action research tool; the remedial instruction action research tool and the intensive coaching action research tool (subdivided into two tools i.e. head teacher coaching event log and school based community of practice event log). The EGR core methodologies action research tool assesses the teacher’s perception of the Teacher Guide usability, level of macro pacing, implementation of lesson plan elements, implementation of core EGR methodologies and tracking of instructional adaptations by the teacher. The remedial instruction research tool tracks the teacher’s perception to remedial instruction, the implementation of group-based instruction as well as in-class assessment. The intensive coaching action research tool tracks teacher’s perception of intensive coaching and keeps a log of head teacher instructional coaching events in addition to school-based community of practice activities. The action research tools are designed to be deployed electronically in order to seamlessly incorporate extra data quality standards and innovations like the Stalling’s classroom observation snapshot (Stallings and Kaskowitz, 1974 ). The project also developed the action research process flow guidelines to guide data collection activities.

The presentation summarizes a paper by Tim Slade and Luis Crouch on the measurement of learning inequality before and after a successful reading project. The paper concludes that at least for the case studied, the project improved not only the averages but also reduced the inequality. The paper was prepared under the auspices of a conference on "Learning at the Bottom of the Pyramid" organized by IIEP and Dan Wagner of U Penn. This is the presentation that was delivered at vCIES 2020.

The Tusome Early Grade Reading Activity is USAID’s flagship education program in Kenya. This CIES 2019 presentation shares the findings from research currently in progress to analyze Tusome’s 2017 lesson observation data, and shares lessons learned from: designing a coaching program to operate at scale; effectively combining incentives and sanctions to drive coaching activities; and effectively combining automated, moderately high-tech data pipelines with qualitative, low-tech feedback on coaching of coaches.

Published abstract: "Summer learning loss – decreased academic performance following an extended school break, typically during the period after one grade ends and before another grade starts – is a well-documented phenomenon in North America, but poorly described in sub-Saharan African contexts. In this article, we use the term ‘grade-transition break’ loss in lieu of ‘summer’ loss to refer to the period after one grade ends and before another grade starts. This study analyses data from early grade reading assessments in Malawi, estimating statistically significant average reductions of 0.38 standard deviations (SD) across several measures of reading and pre-reading skills during two grade-transition breaks. The data show the loss in reading skills during the extended breaks between grades 1 and 2 and between grades 2 and 3 in two consecutive years. The study found no gender-based differences in loss. The findings suggest a need for early grade reading interventions to develop and evaluate mitigation strategies lest significant proportions of within-year performance gains be lost over the break between academic years."