By Satender Rana and Sreenivasa Madenahally
Most of the world’s trillion-dollar companies thrive on the power of data. The advent of ChatGPT is the most recent example of how data is changing the world by increasing individual as well as mass productivity and efficiency. While data is oiling the engine of growth for for-profits, the social sector is trying to ‘catch up’ in leveraging it for greater social impact. A tremendous amount of data is generated in the social sector, and evidence-based policy and programming have emerged practically as a norm in the social sector. But the usage of data is still limited by data availability, accessibility and quality, as well as the lack of capacity and dearth of resources.
Data analytics and education outcomes
India has made significant strides in improving access to education over the past few decades. This has mainly been possible through the Right to Education (RTE) Act, the National Education Policy (NEP) 2020, technological advancements towards promoting e-learning and increased government spending in the Union Budget.
But there are significant gaps in education outcomes, particularly in learning outcomes and school drop-outs. Data analytics can provide insights and enhanced visualisations across socio-economic indicators and empower SPOs, researchers, educators and policymakers to understand the current and historical status and trends to devise policies, strategies and decision-making.
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1. Identifying and addressing the educational achievement gaps: Data analysis can identify disparities in education and provide targeted measures to address them. For example, low female representation in IITs led the government to mandate a 14% reservation of seats for women. Data monitoring ensured progress towards the 20% target for female participation by 2020. Availability of data not only ensures monitoring the progress of a policy measure, but also responds to the needs of historically-deprived groups.
2. Initiating effective teaching methods and practices: Data analytics can model student performance by considering demographic and socio-economic profiles, student behaviour and accessibility to education and nutrition status. This approach can inform curriculum requirements and specialised teaching approaches. For example, ASER reports and the National Achievement Surveys (NAS) consistently bring to light the gaps in learning outcomes and teaching provisions. It can also help SPOs evaluate teacher performance and identify areas for support and capacity building. By leveraging data analytics, schools can support mass customisation of education and enable each student to reach their maximum potential. For instance, a school district can improve students’ reading skills by analysing their reading performance data, designing personalised learning paths and evaluating the effectiveness of programmes through data analytics. This approach can enhance learning outcomes and foster programme improvement.
3. Track student progress and provide personalised learning experiences: Educators and organisations can tailor their instruction to meet each student’s unique needs by analysing data on individual student’s learning patterns and preferences. For example, the formative assessment, collecting data during the learning process, can help teachers detect patterns and determine the areas where students require improvement.
4. Initiate collaborations and partnerships: With increasing emphasis on using credible data for social good and policymaking, there is immense potential for building collaborations between the sector and government and private sector stakeholders. SPOs can identify potential technical partners and collaborators, and allocate adequate resources based on insights and data visualisation. There is tremendous scope for SPOs to leverage their knowledge of social issues and define impact, with tech experts and companies helping them find patterns in data, and modelling complex issues.
5. Better access and inclusion in education: Disaggregated data on gender, age, grade, income, rural-urban and children with special needs (CWSN) reveal groups excluded from quality education. It informs resource allocation and targeted interventions where most needed. For instance, the Unified District Information System for Education Plus (UDISE+) data reported only 48.31% of schools had ramps with handrails for CWSN during 2020-21. Disaggregated data uncovers inequalities that aggregated data cannot. But there are significant challenges to using data analytics in education. The social sector needs to be equipped for mapping and using data. The ability to make data-driven decisions in development organisations is hampered by factors such as the need for more updated and quality data regarding granularity, credibility and reliability. Additionally, organisations, particularly those working at the grassroots level, need more expertise to leverage, manipulate and use data to make informed decisions.
Need for a single data platform
There is a need for a single platform where individuals, organisations and policymakers can access data from a variety of different, credible sources towards data-driven decision-making and programmes. Investing in technology and infrastructure for data collection and analysis can help organisations overcome barriers and challenges in using data. Collaboration between organisations, government departments and research bodies can harness data for enhanced social impact.
Satender Rana is project lead, Knowledge Institute, and Sreenivasa Madenahally is centre lead, Centre for Data Science and Social Impact, who are part of the Global Knowledge Hub at the Indian School of Development Management, Noida