Applying Machine Learning to Understand Different Factors that Influence School Performance

Local Chapter Kiambu, Kenya

Coordinated byKenya ,

Dishan Otieno
, Samwel Jane

Status: Completed

Project Duration: 27 Nov 2021 - 25 Dec 2021

Open Source resources available from this project

Github Repository

Project background.

According to the Kenya National Bureau of Statistics (KNBS) report 2019, Kiambu County has a population of 2,417,735. Kiambu County’s website states that there are 1225 primary and 303 secondary schools. The number of pre-schools has not been declared on their website. The gross and net enrolment rates for primary are 109.6% and 99.7%, while secondary levels are 69.3% and 61.8%, respectively. It is important to note that the increasing population of Kiambu has necessitated more investment in the education sector.

According to Kiambu County’s website, the pre-schools need more investments in public ECD centers to ensure children from poor backgrounds get access to early education without much strain. Primary schools need to invest in providing additional education facilities because of the increasing number of schools going population. On the other hand, secondary schools need significant investment in the education sector to ensure the completion rate reaches 100 percent since it currently stands at 92.5 percent.
Based on the statistics and recommendations outlined coupled with the introduction of the Competency-Based Curriculum (CBC), there is a clear indication that the quality of education in Kiambu needs to be improved. Therefore, this study seeks to investigate the factors and additional resources required to improve the quality of education.

The problem.

With the enrollment of free primary and secondary school education, the number of students enrolled per category has risen. The government has also introduced the CBC, which requires more facilities in schools. The resources in schools are not enough to guarantee better performance. Therefore, this project aims to determine how different factors have affected learning institutions and performance over the years and recommend steps to decide on how to distribute them.  The Country claims more resources are needed in schools to improve performance even as the government tries to achieve 100% transition.

Project goals.

- Develop a dashboard that shows the facilities to students ratio and the distribution rate of facilities in the three categories of schools in the county.
- Build a machine learning model that analyzes the performance of the school and the level of facilities in different academic years.
- Compare performance, number, and type of facilities in public and private schools.
- Outline recommendations on which facilities the county and other education stakeholders should prioritize to improve school performance and achieve 100% transition across the levels.

Project plan.

  • Week 1

    – Data Extraction
    – Data Understanding
    – Data Exploration

  • Week 3

    – Feature Engineering
    – Model selection techniques
    – Modeling

  • Week 4

    – Model Evaluation
    – Report writing
    – Presentation

Learning outcomes.

1. Recommend a check sheet that education stakeholders can use while allocating money for infrastructure development in schools.

2. Visualizations of the distribution of facilities, student population, and performance in the county.

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