Evaluating students' academic performance and the teaching methods used in Ugandan primary schools.

Abstract

Due to the large student population in Ugandan schools, it is challenging for instructors to know each student’s specific talents and shortcomings; as a result, children do not receive the attention they deserve. The project tried to cover this gap between the teachers and students in a way that will map out each student’s weaknesses and strengths in their individual subjects. The main objective was implementing a machine-learning model, especially of the classification type that can discover patterns, analyze, evaluate data and help boost students, performance through classification and prediction. The specific objectives were to collect data regarding educational patterns, to get useful knowledge from accessible data and to uncover hidden trends and patterns using the available data. We carried out analytical research which involved scrutinizing and evaluating available data in this field of research. We studied systems that carry Educational Data Mining that existed before this project. We used xAPI-Edu dataset from Kaggle website which is the dataset for training our machine-learning model. The expected outcomes from this should be that the instructor has to be able to know and understand the weaknesses and strengths of each student in each individual subject.