This study focused on identification of mobilization initiatives for enhancing student’s enrolment into Vocational and Technical Education (VTE) programs in Nigerian Universities. Three research questions guided the ...This study focused on identification of mobilization initiatives for enhancing student’s enrolment into Vocational and Technical Education (VTE) programs in Nigerian Universities. Three research questions guided the study while three hypotheses were formulated and tested at 0.05 level of significance. The study was carried out in South East, Nigeria. Population for the study was 1340. Sample for the study was 753 obtained through proportionate (30%) stratified random sampling technique. A 38 item questionnaire was developed and used to collect data. Data obtained were analyzed using mean and standard deviation to answer research questions while t-test statistic was used to test hypotheses at probability of 0.05 level. It was found out by the study that 38 mobilization initiatives could be used to enhance students’ enrolment into Vocational and Technical Education Programs in Nigerian Universities. It was therefore recommended that the identified mobilization initiatives be implemented by relevant stakeholders to enhance secondary school students’ enrollment into VTE programs in Nigerian Universities.展开更多
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This study focused on identification of mobilization initiatives for enhancing student’s enrolment into Vocational and Technical Education (VTE) programs in Nigerian Universities. Three research questions guided the study while three hypotheses were formulated and tested at 0.05 level of significance. The study was carried out in South East, Nigeria. Population for the study was 1340. Sample for the study was 753 obtained through proportionate (30%) stratified random sampling technique. A 38 item questionnaire was developed and used to collect data. Data obtained were analyzed using mean and standard deviation to answer research questions while t-test statistic was used to test hypotheses at probability of 0.05 level. It was found out by the study that 38 mobilization initiatives could be used to enhance students’ enrolment into Vocational and Technical Education Programs in Nigerian Universities. It was therefore recommended that the identified mobilization initiatives be implemented by relevant stakeholders to enhance secondary school students’ enrollment into VTE programs in Nigerian Universities.
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.
