Data for Development: Enhancing Foundational Learning in Kenya Amidst Divisions – Accessibility, Evidence Gaps, and Strategic Interventions

• In Event: Unlocking Foundational Learning Data in Africa: Bridging Data Silos for Equity and Peace

Proposal

Kenya's foundational learning (FL) landscape is marked by a notable shortfall in open access, with data held by the government unavailable to non-state entities. Although data is generated extensively, it suffers from a significant lack of critical analysis, resulting in the absence of actionable trends or insights. The movement of evidence is severely obstructed by a complicated network of barriers: organisational silos paired with a culture of non-sharing; technical issues regarding data security and quality; systemic power disparities and a fear of data politicisation; and a restrictive legal framework that discourages data sharing. Additionally, current knowledge is not disaggregated in a manner that exposes inequalities, hindering targeted interventions.

Through UDI, an Evidence Gap Map was developed to gain deeper insights into the foundational learning ecosystem. The Evidence Gap Map revealed a surprising deficiency in rigorous, causal evidence for interventions that are widely used and theorised. Despite their prevalence, there is insufficient evidence regarding the impact of remedial learning, technology-enabled learning, school feeding programs, and the influence of the built environment on FL outcomes. The absence of behavioural or cross-sectional methodologies indicates a significant research gap that fails to capture real-time behavioural data or broader trends within populations. The lack of rigorous experimental and quasi-experimental designs, which are vital for establishing causal relationships, is often noted, whilst longitudinal studies that monitor outcomes over time remain uncommon. This methodological disparity raises concerns about the strength of the evidence, as the limited use of experimental and longitudinal research hampers the ability to draw clear conclusions regarding the efficacy of interventions. Enhancing the application of a variety of high-quality methodologies will be essential for producing more trustworthy and actionable insights in future research.

To address the outlined data and evidence limitations in FL, we are partnering with Kenya’s Ministry of Education to implement DBIR in five pilot counties: Nairobi, Kajiado, Marsabit, Kisumu, and Kirinyaga. These Counties were strategically selected to ensure geographic, demographic, and equity representation—including urban, peri-urban, rural, and marginalised regions like Marsabit. Marsabit will serve as the prototype location to develop and test a localised data-sharing dashboard for FL indicators, aiming to improve data use and evidence-based decision-making at the subnational level. The DBIR cycle 1 has provided several lessons from Marsabit County: The subnational officers do not exhibit a strong intrinsic demand for data; instead, the motivation for data collection stems from national compliance rather than local decision-making, obscuring challenges specific to contexts such as nomadic populations. Nevertheless, an increasing interest in utilising existing data was identified, suggesting potential for improvement if capacity is enhanced. There is a noteworthy gap between evidence and its application, which hinders the conversion of raw data into valuable insights for practitioners. The process of data collection is fragmented because actors utilise incompatible tools, leading to redundancies and placing additional burdens on schools. Significant gaps in capacity are apparent, characterised by the absence of dedicated data personnel, inadequate infrastructure, and a lack of skills necessary for basic data management, culminating in poor-quality and unreliable data.

Authors

• Charles Munene Gachoki, Zizi Afrique Foundation

• Maurice Mutisya, Zizi Afrique Foundation