There is a growing body of literature that recognizes the importance of data mining in educational systems. This recognition makes educational data mining a new growing research community. One way to achieve the highe...There is a growing body of literature that recognizes the importance of data mining in educational systems. This recognition makes educational data mining a new growing research community. One way to achieve the highest level of quality in a higher education system is by discovering knowledge from educational data such as students’ enrollment data. Many mining tools that aim to discover exciting correlations, frequent patterns, associations, or casual structures among sets of items in educational data sets have been proposed. One of the widely used tools is association rules. In this paper, the Apriori algorithm is used to generate association rules to discover the importance and correlation between factors that influence student’s decision to enroll in higher education institutions in Sudan. The algorithm is applied using a student’s enrollment data set that was created using a questionnaire and 800 students enrolled in governmental and private sector universities as a sample. This paper classifies factors that influence enrollment into: student’s demographic factors and four categories of enrollment related factors (Student and Society, Educational Institution, Admission, and Employment related factors), and determines the most influential factors in determining student’s decision to enroll in Sudanese universities. The analysis result shows that the Educational Institution related factors (50%) and Admission related factors (40%) are strongly influencing students’ enrollment decision, while the Employment related factors (10%) and Student and Society related factors (0%) have weak influence. The factors out of the 14 Educational Institution related factors that have a high impact are: reputation, diversity of study, quality of education, education facilities, and feasibility.展开更多
文摘There is a growing body of literature that recognizes the importance of data mining in educational systems. This recognition makes educational data mining a new growing research community. One way to achieve the highest level of quality in a higher education system is by discovering knowledge from educational data such as students’ enrollment data. Many mining tools that aim to discover exciting correlations, frequent patterns, associations, or casual structures among sets of items in educational data sets have been proposed. One of the widely used tools is association rules. In this paper, the Apriori algorithm is used to generate association rules to discover the importance and correlation between factors that influence student’s decision to enroll in higher education institutions in Sudan. The algorithm is applied using a student’s enrollment data set that was created using a questionnaire and 800 students enrolled in governmental and private sector universities as a sample. This paper classifies factors that influence enrollment into: student’s demographic factors and four categories of enrollment related factors (Student and Society, Educational Institution, Admission, and Employment related factors), and determines the most influential factors in determining student’s decision to enroll in Sudanese universities. The analysis result shows that the Educational Institution related factors (50%) and Admission related factors (40%) are strongly influencing students’ enrollment decision, while the Employment related factors (10%) and Student and Society related factors (0%) have weak influence. The factors out of the 14 Educational Institution related factors that have a high impact are: reputation, diversity of study, quality of education, education facilities, and feasibility.
