Medical educators and programs are deeply interested in understanding the longitudinal developmental trajectories during the journey from being medical students to competent physicians. Researchers projected various models about learning curves of this journey with the purpose to enhance teaching, assessment, and research activities effectively (Howard, et. al., 2021; Pusic, et. al., 2014). In the same way, it is also important to understand learning trajectories of various subgroups within the population during this journey because it is very likely that students and trainees do not achieve competencies at the same speed and one learning curve oversimplifies the complexity. Because students and trainees have different characteristics and they frequently do not learn and progress at the same pace, it is important to examine whether student subgroups exist so their academic needs can be addressed to create more equitable learning opportunities. By doing so, appropriate resources and interventions can be provided to support learning and progression accordingly. As such, this study intended to explore students’ developmental trajectories and detect group differences based on their coursework performance in anatomy at the preclinical education in one medical school in the middle west of United States using latent growth mixture models. The study also drilled down to investigate how students’ background information and standardized scores were related to their group differences. The study used latent class growth analysis, an under-researched area, to identify unobservable student subgroups and detect group differences on the student growth trajectory.
Learning trajectories and latent growth modeling
Various research examined learning curves and described the rate of learning over an extended period of time in the areas such as psychology, medicine, and education. For example, Pusic and his collaborators (2017) monitored learning processes of 38 participants including medical students, residents, and fellows. They identified learning curve statistical models and recognized the complexity of group and individual trajectories within the same ‘‘multi-level’’ model. Jackson and Sher (2005) identified different subgroups and concluded heterogeneity of trajectories in developmental course by examining initial level (intercept) and growth of alcohol involvement using a growth curve modeling approach. Using latent growth modeling, Costa et. al., (2013) found undergraduate medical students’ empathy, controlled by gender, openness, and agreeableness, did not decline over time. Overall, these theoretical and empirical publications indicate that assuming homogenous of the whole class population of medical students oversimplifies the complexity of growth trajectories among members of different student subgroups.
The literature shows that latent growth mixture modeling provides a promising approach to fully understand students’ growth trajectories and under-observed heterogenous groups within the whole population over time. Latent growth modeling such as latent class growth analysis (LCGA) is a technique to obtain information about inter-individual differences in intra-individual change over time (Nesselroade, 1991). The focus is to examine the relationships among individuals and stratify individuals into distinct groups or categories based on individual response patterns so that individuals within a group are more similar than individuals between groups (Jung & Wickrama, 2008). LCGA generally requires an extensive dataset which might include multiple cohorts and longitudinal data (MacCallum & Austin, 2000). LCGA is different from conventional growth modeling in that it assumes that individuals come from different subgroups and that a single growth trajectory cannot adequately approximate an entire population. The student characteristics (e.g., prior knowledge, socioeconomic classes, gender, and learning styles) that affect the growth trajectory influence each individual in a different way.
LCGA is generally used for the exploratory, post hoc purpose when researchers are not certain about the underlying drivers of distinct developmental courses (van der Nest, et. al., 2020). LCGA assumes that the population is heterogeneous, and it consists of distinct, latent subgroups or classes. It is conducted through the inclusion of K latent classes with different mathematical models for the trajectory. The assignment of individuals to different classes is based on the degree of similarity of developmental courses among individuals. LCGA assumes that the error variance to be the same for all classes and all time points. The identification of these classes allows for discrete individual differences by letting fixed effects (given by the trend) different between classes. In other words, while one class might have a linear, increasing growth curve, the other remains steady or has a linear, decreasing growth curve.
Context
Central Michigan University College of Medicine (CMED) has established since 2013 with a mission to improve access to high-quality health care in Michigan emphasizing rural and medically underserved regions. CMED has about 104 students per class with generally equal number of genders. Students come from different undergraduate and/or graduate academic backgrounds such as mathematics, biochemistry, business, neuroscience, and physics. Its preclinical education lasts for two years, and the curriculum is delivered to students through “a series of integrated, interdisciplinary, systems-based course blocks during the pre-clerkship curriculum” (Thompson, et. al., 2020). Nine foundational and organ-system courses (e.g., Foundational Sciences, Cardio/pulmonary Wellness and Disease, Neuroscience Behavior, and Hematology) are designed and delivered with deliberate considerations of the sequence, breath, and depth of topics, along with two longitudinal courses focusing on medicine in society and essential clinical skills in Year 1 and Year 2. Anatomy has been embedded in all the nine courses of the integrated curriculum.