## 1 Introduction  
Students may vary their enrollment status each semester throughout their academic career, including part-time, full-time, or halting enrollment [1, 2, 3, 4, 5]. A survey conducted at 253 academic institutions in 2017 showed that more than 53% of students switch their enrollment status at least once during their education; only 29% committed to a full-time status every semester, and only 18% maintained a part-time enrollment over their entire academic career [6]. Academic institutions consider part-time enrollment status as a risk factor to students' success. [PERSON] et al. [7] show that, on average, full-time college students have higher retention rates and GPAs at the end of their first academic year compared to their part-time counterparts. In another study, [PERSON] et al. [8] analyzed how academic and demographic features such as age, race, enrollment status, GPA, and financial aid impact students' persistence. Their results suggest that GPA and enrollment status incur the highest impact on college persistence. In [9], [PERSON] argues that course-load is another critical factor affecting students persistence. Her study shows that students who register for more creditsduring their first semester are more likely to complete all required credits for their program, and ultimately their degree. Despite the evident importance of enrollment status to student success, no clear definition outside the semester-based academic designation exists for what it means to be a part-time or full-time student. Such a binary designation appears to be overly simplistic as most students switch between full-time and part-time enrollment statuses during their academic career; as such, we believe there is value in understanding more complex enrollment patterns and their relationship to student outcomes. This assertion is supported by a nation-wide study in 2015 indicating that student success can be achieved through mixed enrollment strategies [10]--the authors report that among non-first-time-in-college students that attend college, those who utilize a combination of full-time and part-time enrollment are more likely to complete degrees when compared to full-time students. In this study, we build an advanced machine learning model to offer a more comprehensive methodology for identifying and grouping students based on their enrollment strategy. Unlike the commonly used single-period model in which a student's enrollment strategy is equivalent to the observed semester enrollment status (i.e., part-time or full-time), we apply a multi-period dynamic approach using a Hidden Markov Model (HMM). Leveraging the proposed HMM model, a major contribution of this paper is a richer understanding of student enrollment patterns. Our model extends traditional categorizations by including the mixed enrollment strategy. Traditional models classify a student as mixed if the student has both full-time and part-time enrollment in their academic record. Our proposed model identifies the mixed enrollment category based not only on how many of a student's semesters were full- or part-time but also on the order in which they appeared. In other words, students who use a mixed enrollment strategy regularly intersperse full-time and part-time semesters. After categorizing students into one of three groups (full-time, part-time, or mixed enrollment strategy), we examine the academic outcomes (GPA, DFW, and graduation rates) associated with each strategy. Accordingly, our second research contribution is reporting the correlation between academic engagement (via enrollment strategies) and different measures of academic success. The remainder of the paper is organized as follows: In Section 2, the research questions and corresponding contributions are explained. Section 3 reviews the existing body of research on factors that impact students' academic performance. Section 4 explains the applied methodology for clustering students based on their enrollment strategy. A brief description of the data used in this paper is provided in Section 5.