Box 3: Data for Disability in Education

## **In Indonesia, there are three data sources that capture information on students with disabilities:**

- Dapodik by MoEC: Dapodik captures the prevalence of disability variables among students on visual/auditory/motor-sensory dimensions, as well as gifted children, and those with learning difficulties, Downs syndrome, and autism.

- EMIS by MORA: EMIS currently includes data on children with disabilities in all MoRA schools along the following dimensions: physical impairments including visual, auditory, motor-sensory. Data are also collected on behavioral and learning challenges, such as the ability to concentrate, as well as behavioral issues ( _lamban belajar, sulit belajar dan gangguan komunikasi_ ).

- SUSENAS: SUSENAS also captures data on visual/auditory/motor-sensory dimensions for students, in addition to behavioral and learning challenges. Additionally, SUSENAS tracks both “inability to understand communication” and “self-care” ( _kesulitan/ gangguan berbicara dan atau memahami/ berkomunikasi dengan orang lain_ and _kesulitan/ gangguan untuk mengurus diri sendiri_ ).

**However, data verification across the three sources is difficult, as different terms are used to categorize disabilities. Furthermore, data quality issues exist as a result of unclear guidelines and a lack of understanding on the part of data operators to properly record disabilities.** For example, Dapodik may not properly classify children with Down syndrome in the right category, Susenas may include children with Down syndrome in “inability to understand communication”, and EMIS may put them in the “other health problem” category. Similarly, children with the same issue may be classified by one school under “behavioral issues” and another school may classify them under “inability to concentrate.” There also do not appear to be technical guidelines for operators to classify students, and even if there were technical guidelines, it is not clear that operators are qualified to make such classifications.<sup>1</sup>

**To address data quality and verification issues, MoEC and MoRA are currently working with staff from DFATfunded programs INOVASI and TASS on testing an approach to teacher-led disability identification.** This uses a form and an app called the _Profil Belajar Siswa_ (PBS), or Student Learning Profile. This is based on the UNICEF/Washington Group Child Functioning Module and will enable comparability across Dapodik, EMIS and BPS data sets. Guidelines for teachers have been developed and undergone several technical review processes by MoEC, MoRA and relevant education and disability experts. The system is currently being piloted initially in Lombok, East Java and Nusa Tenggara Timur.

# 5. Conclusion and Recommendations

While females are on average above males in terms of enrollment and learning at the national level, there is wide variance across indicators sub-nationally at all levels of education. This variance includes Kotamobagu, North Sulawesi, where primary enrollment favors females with a GPI of 1.15, to Central Buton, West Sulawesi, where the GPI is 0.8, favoring males. Wonosobo District is another example, where the NER in junior secondary for females is 21 percentage points higher than for males, to Wajo District, where female enrollment is 26 percentage points lower than for males. Student learning performance also varies and, while girls uniformly outperform boys, the size of the female-to-male score gap varies widely.

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