Forecasting Student Academic Performance in Kenyan Secondary Schools Using Data Mining


Authors : Terrence Njiru Kananda; Henry Mwangi

Volume/Issue : Volume 8 - 2023, Issue 3 - March

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://bit.ly/3lYk5uL

DOI : https://doi.org/10.5281/zenodo.7793063

Abstract : - Stakeholders in Kenyan education are concerned about student performance. Data mining has emerged as an alternate method for education stakeholders to employ in making decisions about student performance in their final year exam. Kenya's education sector provides a wealth of statistical data that might provide vital information about students. Information and communication technology collects and compiles low-cost data that can be used to forecast student performance. However, no meaningful information is extracted from this data by Kenyan educational institutions. In this paper, we propose and develop a prediction model for forecasting Kenya secondary school learner performance utilizing prior performance data from students, which will be transformed and cleaned before being used in training and testing the model. Our model employs data mining techniques to improve forecast accuracy. We will present the model theoretical framework, conceptual framework, and outcomes.

Keywords : DM - Data Mining, EDM – Educational Data Mining, KCPE – Kenya Certificate of Primary Education, KCSE – Kenya Certificate of Secondary Education

- Stakeholders in Kenyan education are concerned about student performance. Data mining has emerged as an alternate method for education stakeholders to employ in making decisions about student performance in their final year exam. Kenya's education sector provides a wealth of statistical data that might provide vital information about students. Information and communication technology collects and compiles low-cost data that can be used to forecast student performance. However, no meaningful information is extracted from this data by Kenyan educational institutions. In this paper, we propose and develop a prediction model for forecasting Kenya secondary school learner performance utilizing prior performance data from students, which will be transformed and cleaned before being used in training and testing the model. Our model employs data mining techniques to improve forecast accuracy. We will present the model theoretical framework, conceptual framework, and outcomes.

Keywords : DM - Data Mining, EDM – Educational Data Mining, KCPE – Kenya Certificate of Primary Education, KCSE – Kenya Certificate of Secondary Education

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