Leveraging UDISE+ SDMS Data for CWSN Identification and Monitoring

Challenges in Accuracy and Disability-Specific Reporting

Introduction

The Unified District Information System for Education Plus (UDISE+), integrated with the School Data Management System (SDMS), serves as a cornerstone for India’s school education ecosystem. SDMS enables real-time data entry at the school level, feeding into UDISE+ for comprehensive analytics. For Children with Special Needs (CWSN) – encompassing students with physical, sensory, intellectual, and multiple disabilities – these systems are indispensable for identification, enrolment tracking, and monitoring progress toward inclusive education goals under the National Education Policy (NEP) 2020 and Sustainable Development Goal 4 (SDG-4). The UDISE+ 2024-25 report highlights 2,149,258 CWSN enrolments (0.87% of total 246,932,680), but realizing the full potential of this data requires extensive utilization for targeted interventions. This article emphasizes the strategic use of UDISE+ SDMS data, while addressing critical concerns around data accuracy and the evolution of reporting on the nature of disabilities.

CWSN Elementary Enrolment UDISEplus 2024-25

CWSN Secondary Enrolment UDISEplus 2024-25

Extensive Use of UDISE+ SDMS Data for CWSN Identification and Monitoring

UDISE+ SDMS data, collected annually through an online Data Capture Format (DCF), provides granular, school-level insights that can transform CWSN support. Key applications include:

• Identification and Enrolment Drives: SDMS captures student profiles, including disability status, enabling block and district-level mapping. For instance, integrating SDMS with Samagra Shiksha’s identification camps (funded at Rs. 10,000 per camp) allows for proactive screening, targeting the out-of-school CWSN. Real-time dashboards can flag low-enrolment districts, such as Meghalaya (0.32% CWSN share), for immediate action.
• Monitoring Retention and Outcomes: Longitudinal tracking via unique student IDs in SDMS monitors transitions (e.g., from primary to secondary, where CWSN enrolment drops to 0.87%) and performance indicators like dropout rates. States can use this to allocate aids/appliances (Rs. 3,500 per child annually) and track Individualized Education Plans (IEPs).
• Resource Allocation and Policy Formulation: Aggregated SDMS data informs infrastructure upgrades, such as CWSN-friendly toilets. By linking with population projections, it supports forecasting needs, ensuring 100% accessibility by 2030.

Extensive use – through API integrations with health systems for disability certification – can boost CWSN enrolment from 0.87% to the targeted 2.2% prevalence rate, fostering equity.

List of Tables Contents on CWSN  UDISEPlus 2024-25

Challenges in Data Accuracy: Lack of Expertise Among Reporting Respondents

Despite its potential, UDISE+ data accuracy remains a bottleneck, primarily due to the limited expertise of respondents (teachers) – often school heads or clerical staff without specialized training in disability identification. The DCF requires flagging CWSN based on self-reporting or basic observation, but without standardized tools like the WHO’s Disability Assessment Schedule, mis-classification is rampant:

• Under-Reporting and Over-Reporting: Rural schools, handling 70% of CWSN, frequently under-report due to stigma or unawareness (e.g., mild intellectual disabilities overlooked). Conversely, generic labelling inflates figures without nuance, as seen significant discrepancy between the UDISE+ and Census 2011 estimates.
• Training Gaps: The majority of teachers are yet to receive in-service training on inclusive education, leaving respondents ill-equipped. This leads to inconsistencies, such as gender disparities exacerbated by cultural biases against reporting girls’ disabilities.
• Verification Mechanisms: While SDMS includes Aadhaar linkage, field audits are infrequent, eroding reliability. Recommendations include mandatory certification modules in DCF training and AI-assisted validation to enhance accuracy.

Addressing these requires capacity-building under Samagra Shiksha, ensuring data drives evidence-based interventions rather than flawed planning.

Evolution of CWSN Reporting: From Disability-Specific Data to Aggregated Counts

Historically, UDISE (pre-UDISE+ era) and early UDISE+ iterations reported CWSN data disaggregated by nature of disability (e.g., visual, hearing, locomotor, intellectual, as per RPWD Act, 2016’s 21 categories). This enabled tailored supports, like Braille resources for visual impairments. However, recent summary reports, including UDISE+ 2024-25, present only aggregated enrolment by gender and level, omitting type-specific breakdowns. This shift prioritizes brevity in national highlights but limits analysis for state-level planning.

Verification: Is Disability-Type Data Still Collected?

Upon review of UDISE+ guidelines and data capture formats, yes, detailed data on the nature of disability is still being collected at the source. The SDMS DCF  includes fields for “Type of Disability” in the student module (e.g., codes for locomotor, visual, hearing, etc.), as confirmed in the 2022-23 Data Capture Format and ongoing KPI documentation. Studies like the 2025 JAMA Network analysis utilize this raw data for district-level distributions by disability type, indicating its availability in the backend database. However, it is not published in the annual summary report’s tables, possibly to streamline reporting or due to data sensitivity. Access to disaggregated data requires requests via the UDISE+ portal, supporting research but hindering routine policy use.

This collection ensures continuity, but reinstating type-specific tables in future reports would enhance transparency and targeted interventions.

Concluding Observations

UDISE+ SDMS data holds transformative power for CWSN identification and monitoring, enabling a shift from reactive to proactive inclusion. Yet, accuracy hinges on empowering respondents with expertise through targeted training and verification. While aggregated reporting has simplified overviews, the ongoing collection of disability-type data underscores UDISE+’s depth – advocates must push for its fuller publication to align with RPWD Act mandates. By 2030, integrating SDMS with digital tools could achieve 95% accurate tracking, ensuring no CWSN is left behind in India’s education journey.

Suggested Readings

• Ministry of Education, Government of India. (2025). UDISE+ 2024-25 Report.
• Ministry of Education. (2022). UDISE+ Data Capture Format 2025-26.
• Singal, N., et al. (2025). “Disability Among School Children Across Districts of India.” JAMA Network Open.
• UNICEF. (2023). Disability-Inclusive Education Practices in India.