Academic performance prediction using data mining techniques: Identification of influential factors effecting the academic performance in undergrad professional course

Abstract

Educational data mining is used to convert the randomly available data in educational settings into some beneficial information. It helps in building insights for different research questions that arise in educational settings like performance prediction of students in academics, designing of new courses, instructors� feedback, method or mode of teaching, etc. This paper aims to answer questions that has been a major challenge for researchers, i.e. the huge list of drop out rate and lower percentage of first-year students. It highlights factors that affect the performance of students. There are a lot of studies that has been conducted in the field education like psychology and statistics. This case study targeted students enrolled in Bachelor of Computer Applications (BCA). The aim of our research work was to show the impact of variables on academic performance of students. The sample size of the study is 480 students of BCA. The questionnaire is based on factors categorized as Demographic, Academic, Social and Behavioural. The results of the study revealed that family income, parents qualification and interaction with teachers were among the influential factors along with previous year percentage, current year attendance and class behaviour.