## 2 Literature Review  
In higher education research, student retention has been one of the most widely studied topics [22]. Historically, research on dropout has focused on freshmen and sophomore students [5, 23], since the highest rates of dropout correspond to these groups. However, the existence of more rigorous courses after freshman year, may present a barrier to completion for many students, who accumulate credits slowly and finally drop out [6]. Currently, post-freshman year dropout accounts for over 40% of all university dropouts [6].  
High-failure rate courses are common in undergraduate programs and have been linked to dropout and late graduation [8, 24, 25]. There are numerous studies, the majority of which are qualitative, which link unsatisfactory results in freshman high-failure rate courses to dropout [4]. However, the existence of high-failure rate courses beyond the freshman year explains why the variability of student advancement in their program normally increases over subsequent years [6].  
Despite the phenomenon of dropout being a growing concern among governments and policymakers [6, 26], there is no universally recognized definition about this concept [27]. The Center for Higher Education Policy Studies [26] defines dropout rate as the number of students who withdraw from a program of studies, in relation to those who began that program as part of a particular cohort. The aforementioned center, however,claims that this definition is problematic because it ignores students who pull out temporarily (stopout), as well as those who change program or institution (transfer). In this study, dropout students will be considered those who, after a certain date, fail to enroll for subsequent courses within the same program, without having yet completed that program. We will focus on late program dropout, i.e., program dropout that occurs after two or more years of program study.  
Program dropout is a major issue globally, because a portion of students who do not complete a program with a degree, could leave higher education and never graduate from another program or institution [28]. In Chile, the average dropout rate per program for the cohort that entered the university in 2007, reached 57% in the seventh year, and early dropout was 32.6% [29]. Therefore, late program dropout accounted near 25%.  
The different methods provided by Process Mining can be helpful for CA techniques to explore data obtained from educational environments, as long as theoretical frameworks, which are valid for the research community, can support the models of analysis obtained [30]. We consider two frameworks. The Student Motivation and Persistence model [31] states that motivation and persistence depend on four aspects: The goals that lead the student to stay in a given program, self-efficacy, sense of belonging, and perceived worth of the curriculum. Self-efficacy is defined as a person's belief in their ability to succeed in a specific situation [32]. Sense of belonging describes when the students come to see themselves as members of a community who value their participation [31].  
On the other hand, the Investment model [33] explains commitment and the decision to persist in a relationship (in this case, to continue in the study program) based on satisfaction, quality of alternatives, and investment size. Satisfaction and investment have a positive effect on commitment, while the attractiveness of any other alternative has the opposite effect [34]. This method has been used in the past to explain the dropout phenomenon in higher education at the freshman level [35].