Right-fit EdTech: Leveraging Loquot and other (machine) learning to support foundational learning in LMICs [CIES Presentation]

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.