Student Academic Performance Prediction

The Student Academic Performance Prediction dataset captures a wide array of factors influencing student outcomes, with the goal of predicting academic performance. The dataset includes demographic variables (e.g., gender, age, disability, religion), socio-economic factors (e.g., family structure, parental employment and education, fee payment difficulties), and school-related attributes (e.g., type of school, availability of facilities like libraries and labs, school composition, and type of residence). Additionally, it accounts for the student’s learning and assessment styles, participation in co-curricular activities, and factors like school absences and the presence of role models. Academic performance is measured through grades at various stages (Form 1 to Form 4, Mock, and KCSE exams) and is influenced by external factors such as conflicts at home, access to drugs, and challenges during exam periods. The dataset also includes detailed information on the effects of these various factors on performance, providing valuable insights into the multiple dimensions that shape a student's academic success. The source of the student academic performance data was collected between January 2019 and April 2019 using questionnaires from recent secondary school graduates in Kenya. The respondents were randomly sampled from five tertiary institutions namely: university, polytechnic, teacher training college, medical training college and technical training college.