Exploring Tanzanian Education

Mapping education data in Tanzania illustrates the gravity of the current situation while improving evaluation for future investment decisions.

With twenty percent of the government's budget allocated to education—recruiting teachers and building schools—it is increasingly important to encourage education transparency to assess the efficacy of this investment. This site is a tool to explore the education landscape.

  • Context
  • Performance
  • Investment
  • Data
  • ?
  • Poverty

    The highest percentage of the population in the lowest quintile are found in centrally located regions, such as Dodoma, and more sparsely populated coastal areas, such as Lindi.

    This economic disadvantage creates an enormous challenge for enrollment and retention of children in these areas in these areas furthering educational inequality.

  • Gini Coefficient

    Regions with the most rapid population growth, Shinyanga and Mwanza, have the highest gini coefficient, indicating increased levels of inequality. As urban areas grow, it is critical that educational funds and resources are evenly distributed to ensure country wide educational progress.

    Both poverty and inequality can dramatically influence the attendance of students.

  • Net Attendance Rate
    • Primary
    • Secondary

    Although primary school attendance is above 60 percent in all regions, this measure falls dramatically in secondary school, with no region above 30 percent. Regions with the lowest net attendance rates match those with higher incidences of poverty, and reveal a wider gender gap in attendance than the neighboring regions.

    These factors can impact school performance.

  • Literacy

    Learning outcomes, literacy in particular, provide a richer understanding as to the success of an educational system and remain a strong indicator of a child's future.

    While all regions maintain relatively high average literacy rates, only one region, Arusha, has higher female literacy rates than male.

    Exam results can shed further light on this gender gap and support direct evaluation of student performance.

  • 2011 Exam Results
    • Primary
    • Secondary

    Although regions bordering Kenya performed considerably well on primary exams, results vary dramatically throughout the country. Within the central regions, less than half of the students are passing, and secondary school results reveal an even more dismal picture, with pass rates below 60 percent.

    When looking at performance over the last three years, more regions have fewer passing students, both in primary and secondary.

  • Exam Results 2008-11
    • Primary
    • Secondary

    From 2008-2011, many northwest regions greatly improved exam scores for primary, whereas almost all secondary schools show decreased percentages of passing students. Areas of grey or negative change contrasts with the dark orange where there was positive change.

    Find out where government education spending is going.

  • Recurrent Expenditure
    • Primary
    • Secondary

    Regions bordering Kenya, and along the coastline, tend to receive more spending than the central regions and exhibit higher exam scores. This correlation is not maintained in some areas with lower investment, like Mwanza, which holds one the more positive exam results within the country.

    View pupil-classroom ratios to continue to explore the extent of infrastructure investment by the government.

  • Pupil-Classroom Ratio
    • Primary
    • Secondary

    The regions with the highest ratio also have the fastest growing population and lowest literacy rates, demonstrating the imperative need for investment in the growing number of students and their learning environment.

    Pupil-teacher ratios may be more important than access to classrooms for determining the effectiveness of a students education.

  • Pupil-Teacher Ratio
    • Primary
    • Secondary

    The level of personal attention a student can receive is directly related the size of the class. The regions with the highest number of students per teacher also aligns with areas with poorer performance results, despite higher levels of spending.

    See Tabora region where the average ratio is 60 pupils per teacher.

About the site

The Exploring Tanzanian Education site investigates the education sector in Tanzania by visualizing contextual socio-economic data, education outcomes and investment data, to provide insight into the state of education within Tanzania.

Methodology

Data work was carried out using OpenOffice and SQLite to join attribute data with spatial data for the purpose of visualization. All primary and secondary datasets, other than the exam results, were obtained and collected from Tanzania's Ministry of Education and Vocational Training, National Bureau of Statistics, and the Ministry of Finance, with corresponding region names.

Primary School Leaving Evaluation (PSLE) and Certificate of Secondary Education Examination (CSEE) exam results were obtained from the National Examinations Council of Tanzania (NECTA). Primary exam results were listed by region with gender and score categorizations and secondary were listed by school code and name. Percentages of students passing (C or higher for primary, or a passing division of I, II, III for secondary) were calculated for both the PSLE and the CSEE exam results. The primary exam results only contained region location identifiers and did not include a national school identifier code so only region-level aggregations were able to be visualized.

Exam results were obatained in pdf format, and were converted to html, then parsed with a python script that outputted to csv. A list of all secondary schools in 2012, with unique codes and district names, was then joined with the school codes found in the CSEE test results and aggregated by district. Only 75% of school codes matched between the 2012 school list and the 2011 exam school codes, resulting in a loss of accuracy for the aggregate pass rate. Other issues arose with multiple district names, for example, Dodoma, Dodoma Urban, and Dodoma Rural were all present in 2012 secondary school district list, however the only the latter two are considered districts, so only Dodoma Urban and Dodoma Rural were mapped. Averages, minimums, and maximums were calculated to be included in the visualization.

Data was matched to county administrative geographic data by region name. There is no official source of region administrative boundary geographic data. This site used county geographic data made publicly available online.

All maps were created with TileMill and hosted with MapBox. Color scales were determined based on a colorblind-safe color-picker tool within each of the visualized indicators. Photo on data page taken by Wendy Tanner.

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