Modeling Academic Achievement With Respect to Self-Readiness of Public Universities Graduates.

Students' performances in higher education institution are evaluated based on their academic achievement. Despite of having technical skills obtained by the graduates
during their study in university, it is crucial for them to have other additional skills that help them in decision making and problem solving. This study has presented a model of academic achievement with respect to self-readiness of the public universities graduates. Two data mining methods were used in this study such as logistic regression and neural network to obtain the model with the highest accuracy. The selection of data mining approaches was based on the ability of data mining as a powerful tool for academic analysis purposes. In higher educational institution, data mining can be used for the process of uncovering hidden trends and patterns that help them forecast the students' achievement. In this study, a dataset comprises of public higher educational institution
graduates demographics and self- readiness information was analyzed. The results show that descriptive and self readiness produce higher accurate percentage compared to the self readiness alone. The result also find that neural network is the best model to be developed compared to logistic regression while field of study, citizenship, ready to face working world and challenges, problem solving, decision making and group working are the best predictor for academic achievement.