## 5 Discussion and Conclusions  
In practice, the prediction models built in Case 3 are more meaningful than models of Case 1 and Case 2. Therefore, we focus on results of Case 3. In this case, the experimental results showed that prediction accuracy mean rate of RF 10-fold experiments was nearly 79.99%, that of DT 10-fold experiments was 74.59% by C5.0 algorithm and 80.00% by CART algorithm, and that of MLP 10-fold experiment was 69.02%. CART outperforms C5.0, RF, and MLP algorithms. For Case 3, the selected factors, which most influenced freshmen's learning performance were \"Mother's occupations\", \"Department\", \"Father's occupations\", \"Main source of living expenses\", and \"Admission status\". Importantly, the two factors: \"Mother's occupations\" and \"Department\" had the highest significant impact on first-year students' learning performance; whereas four factors: \"Father live or not\", \"Mother live or not\", \"Child of new residents\", and \"Aboriginal\" had the least effect on freshmen's learning performance. The analysis results are expected to be a roadmap for students' early performance prediction so that strategic intervention can be planned before students reach the final semester. The results of prediction model and those discovered to be important factors also can be used as leading indicators to prevent students from being dropped out due to poor learning performance. From the extracted knowledge rules of decision trees, we have discovered some useful information. To predict excellent students, the occupation of parents can determine the academic performance of freshmen students, especially when parents' occupations are government employees and teachers who have higher education backgrounds. Moreover, if the mother is a housewife, it can also contribute to outstanding academic performance. It also could be found that if the financial resource is intact, whether it comes from family supply or scholarships, it will also be quite helpful for students' learning. To predict students of extremely poor academic performance, we also discovered some rules. The technological and vocational universities should focus on transfer students and those students whose living expenses is mainly from part-time jobs. Generally, their learning performance will be poor and they require additional guidance. In this study, we used family background variables, which can be obtained in the beginning of freshmen semester to predict students' learning performance. We can use the established models to predict the academic performance of freshmen as soon as they enter the school. If a student is predicted with poor learning performance, educational teams can carry out early-warning counseling measures, such as reminding class tutors to pay more attention to them. In the case of negative influence of part-time jobs on the absences and poor learning situations, educational teams can offer early remedial teaching resources or teaching assistants for individual tutoring. These proposed measures can effectively prevent these poor students from falling behind in their learning process. For students who are predicted for excellent academic performance, universities can focus on elite-style tutoring, such as special classes for professional and technical advancement, license examination training, entrepreneurial competitions and other employment skills enhancement. For undergraduates who are planning to enter higher education programs, universities can offer more support for foreign language skills development and entrance examinations. In sum, this study successfully built prediction models for freshmen's academic performance using CART, C5.0, RF, and MLP algorithms in a Taiwanese vocational university. Five important features have been determined to take advanced actions for HEIs management. For potential direction of future works, other machine learning algorithms could be applied. In addition, more input variables could be included in the future.