The monitoring framework for school education in Uttar Pradesh collects vast amounts of data on school-level engagement through various mobile applications, visualised on public dashboards such as Prerna Portal. However, much of this data remains under-analysed and underutilised as districts and block officials do not have easy access to raw data and lack the capabilities to process data into meaningful insights. Rarely is it triangulated at the district level to uncover the real relationship between system-level inputs and student learning outcomes, particularly within the NIPUN Bharat initiative.

The CSF team in Jhansi, under the leadership of former District Magistrate Avinash Kumar, IAS, made deliberate efforts to change this. In August 2024, a comprehensive analysis was undertaken using government datasets available at the state and district levels to examine how different input indicators influenced student learning outcomes (SLOs). The primary objective was to identify ‘super-critical’ inputs, those with the most significant potential to improve learning outcomes, so that district resources and focus could be aligned accordingly in order to make students NIPUN.

Approach

To understand what influences student learning outcomes, we began identifying key school-level inputs through a detailed review of existing literature. A list of 24 indicators, such as teacher pedagogical practices, student attendance, school infrastructure, etc, was shared with the state. Each dataset was cleaned to correct errors and then aggregated at the school level. This process led to the creation of a master sheet with 18 input variables and one outcome variable (percentage of NIPUN students at school level in the last quarter of the academic year 2024-25), used as a proxy for student learning outcomes. We then analysed the data to identify meaningful relationships by calculating correlations, retaining only those indicators that showed a significant association with the learning outcomes. Finally, we used simple linear regression to explore how changes in input variables could potentially impact student learning outcomes, helping us estimate the likely effect of a one-unit change in each input while holding others constant.

Findings

I. Pearson Correlations, which measures the strength of the relationship between two variables, highlighted several school-level factors that are positively linked with the percentage of NIPUN students. Notably, total enrollment (Aadhaar verified) showed a positive relationship (coefficient = 0.1788, p = 0.01), suggesting that larger schools with higher verified enrollment may also exhibit better learning outcomes. Additionally, mentor-related inputs, such as total time spent by mentors (0.1211, p < 0.05) and the extent to which lesson plans were used (0.1036, p < 0.05), showed meaningful correlations with student outcomes, indicating the importance of supportive supervision provided by Academic Resource Persons (ARPs) to teachers. Using teacher guides and lesson plans in classrooms also showed a positive correlation with student learning outcomes. Other variables with significant positive associations included the number of tablets available (0.1358, p <0.01), total funding received (0.132, p < 0.05) and student attendance percentage (0.1223, p <0.05). Variables such as Kayakalp score and number of inspection visits did not show any significant influence on student outcomes. Overall, the analysis highlighted the importance of teacher support mechanisms like supportive supervision and school infrastructural resources in improving foundational learning levels.

Please refer to the table below, to see the correlation between two sets of data, i.e., various inputs going to the school and the strength of their correlation with the percentage of NIPUN students

• Number of mentor visits (coefficient = 1.119, p < 0.1) and total time spent by mentors (coefficient = 0.00999, p < 0.05) is statistically significant at the 90% level, indicating their potential relevance in influencing student learning outcomes, indicating increase of one visit per school can potentially increase % of NIPUN students in school by 1.1%
• % times lesson plan used (coefficient = 0.1324, p < 0.05) also showed meaningful positive associations, suggesting that both encouraging teachers to use teacher guides and follow the lesson plan have the potential to contribute to better student performance.
• Total enrollment (Aadhaar Verified) also has a relationship (coefficient = 0.1043, p < 0.01), indicating that an increase in enrollment (bigger size schools) is associated with a higher percentage of NIPUN students.
• % of student attendance (coefficient = 0.2218, p < 0.05) is significantly associated with improved learning outcomes, underlining the importance of regular student attendance in classrooms, indicating 10% increase in attendance can potentially improve student learning outcomes by 2%.
• Number of tablets available (coefficient = 12.94, p < 0.05) and total funding received (coefficient = 0.00019, p < 0.05) are also positively associated with student outcomes, highlighting the potential impact of better infrastructure and financial resources.

What Do These Findings Mean?

The analysis pointed towards various indicators, which could give a signal to district administrators on how to improve SLOs. These findings have helped sharpen the district’s focus on where to intervene:

1. Mentoring matters: mentoring by ARPs, both the frequency of visits and the time spent per school, showed a consistent positive association with learning outcomes over two years. This underscores the need to fill ARP vacancies, systematise visit planning and prioritise mentoring time in low-performing schools.
2. Time-on-task for mentors: higher mentoring time per school correlated with better outcomes, underscoring the need to increase time spent, particularly in lower-performing schools.
3. Classroom practice matters: teachers’ consistent use of lesson plans and teacher guides contributed positively to learning outcomes. Following the teacher guide and lesson plan can potentially improve classroom practices, leading to improvement in student learning outcomes.
4. Attendance is still crucial: student attendance remains a foundational driver for learning gains, validating the push for increased parental and community engagement to improve student attendance in schools.

Data to Action: Formation of NIPUN Action Plan for Jhansi

Based on these findings, the CSF Jhansi district team created a targeted ‘NIPUN Action Plan’, focused on inputs that are directly influenceable at the district level.

Based on findings, the NIPUN Action Plan focused on the following key interventions,

1.  Maximising quality and quantity of supportive supervision
2.  Maximising usage of lesson plans and Sandarshika by teachers
3.  Maximising student attendance through community (parental) engagement

Within the first quarter of implementing NIPUN Action Plan (January – March 2025), Jhansi began witnessing early signals of progress:

• ARP Coverage: schools covered by ARPs rose from 44% (Jan–Mar 2024) to 87% (Jan-Mar 2025), driven by a district-level ARP visit roster.
• Community engagement: a district-wide mega Parent-Teacher Meeting (PTM) on 10 March 2025 saw active participation: 91% of head teachers submitted PTM reports, with 86% student and 46% parent attendance. Senior officials, including the Chief Development Officer, Basic Shiksha Adhikari and Block Education Officers, also took part.

Way Forward

Jhansi’s experience demonstrates how rigorous analysis of existing government data can guide localised, actionable strategies to strengthen the NIPUN mission. By shifting from anecdotal decision-making to data-backed diagnosis, the district has taken a significant step toward unlocking systemic improvements in foundational learning. The model provides a replicable framework, one that other districts can adapt to convert data into contextualised action and action into measurable outcomes. Data-to-action experience in Jhansi encouraged other district teams to undertake a similar exercise in their districts. It is equally important to monitor supercritical inputs in real-time so that timely corrective actions can be taken. Strengthening data systems in districts for informed decision-making is a key change pathway for improving student learning outcomes in districts. Making every child NIPUN is not only about improving classroom outcomes, but about creating the foundation for a skilled, confident and future-ready generation. Jhansi’s journey shows that when data-driven governance is linked directly to classroom practice, the vision of a Viksit Bharat comes within reach.