About 50 percent of all Dutch students enroll in a vocational education program. All efforts of vocational institutes are aimed at guiding students towards a diploma; yet, on average, about 28 percent of the students drop out. The lower the level of education, the higher the likelihood of unemployment (9,3% of all 15- to 25-year-old people are unemployed. When no start qualification is obtained (vocational level 2) the unemployment rate is 12,5% for this group.). Thus, students who drop out of vocational education have a higher risk of unemployment and poverty, which is a major social. Vocational institutes are aware of this social mission, and the life-changing consequences for students with these problems and are motivated to reduce the number of dropouts. Despite the abundance of dropout-related research, most of the articles are related to secondary education, high school, or college. Research on student dropout in vocational education is surprisingly scarce. There is a need for more information about the dropout decision in vocational education and the performance of dropout prediction modeling needs an improvement. Therefore, this dissertation aims to answer the following research questions: - What drivers determine student dropout in vocational education? - How can we improve early identification of dropouts? This dissertation addresses these research questions in five studies. The first three studies relate student expectations, cognitive skills and personality traits, and the differentiation skills of teachers to reduce student dropout. The final two studies use machine learning techniques to identify students at risk of dropping out and offer a systematic way to act on this. Main findings and conclusions The findings show that (1) dropped-out students did not have different expectations, as measured by our expectations questionnaire, about the vocational program than successful students, (2) personality traits and cognitive ability as measured by the formative entry test appear, unlike in other ...