This proposal is a Work in Process (WiP) paper that highlights the research of students pursuing two-year associate's degrees at Ocean County College (OCC), New Jersey, USA. When students enter college or university, they set up schedules to successfully achieve their degree requirements. While there is a prescribed list of classes a student must take to obtain their degrees, the sequencing of those courses, is in large part based on discussions between students and their advisors and/or academic mentors. A full-time course load at an open-enrolment community college is twelve to eighteen credits, which is approximately four to six classes per semester. Within their course maps, students pair different courses together to successfully complete their degrees mainly due to core-requisite and pre-requisite relationships. This research will augment those primary scheduling guidelines based on course pairings that have shown historical correlation for enhanced student success. Studies have shown that students who have interdisciplinary majors and thus engage in interdisciplinary coursework throughout their time in college, tend to obtain benefits such as strong critical thinking skills. This study goes beyond looking at only interdisciplinary course pairings to explore possible student success in pairing STEM (science, technology, engineering and mathematics) courses together, along with traditional interdisciplinary pairings. Class combinations for this research will involve students taking STEM courses with either other STEM courses or STEM courses with non-STEM courses. In the context of this study, student success is determined by course outcomes such as grades, and class withdrawal status.

To explore the impact of existing high occurrence course combinations, the research question in this study is, “how and to what extent do course pairings influence student success for students in STEM courses?” By understanding how student success is affected by course pairings, advisors and academic mentors at this college may be better able to advise students when planning out their courses to achieve their associate’s degree.

To accomplish this, historical student data of first time, full time, fall to fall cohorts at Ocean OCC from Fall 2017 to Summer 2025 will be analyzed. The data collected will include course terms, course sections, and course outcomes; the open-source programming language R, will be used to analyze the data in two phases. Phase I of this research project will identify large frequency co-occurrence course pairings resulting in the creation of a high frequency network and chord-diagram based visualization. Phase II of this research project will utilize student success metrics, such as earned grade per course, to test the strength of correlation between the selected co-occurrence course parings. Initial assessment via matrix plots will be used to identify potential complementary and anti-complementary courses. High potential candidate pairings will then be formally tested for strength of correlation and possible regression models may be generated.

Keywords: Co-occurrence courses, interdisciplinary study, complementary courses, anti-complementary course, open enrolment higher education, community college, R coding language, STEM education, student success.