STEM Enrollment Decision Trees as Graduation Predictors for Community College Students Enrolled in Remedial Mathematics

IF 1.7 Q2 EDUCATION & EDUCATIONAL RESEARCH Community College Review Pub Date : 2024-09-17 DOI:10.1177/00915521241279832
Zachary Richards, Angela M. Kelly
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Abstract

Objective/Research Question: Community college graduation rates are typically quite low, and developmental mathematics enrollment and coursetaking patterns may constrain academic outcomes. To identify ways in which community college graduation rates may be improved, decision trees were utilized to examine the STEM coursetaking patterns of N = 5,065 students who matriculated in remedial mathematics. Methods: The research design was guided by Tinto’s academic and social integration framework, which provided an analytical lens for identifying how decision trees facilitate academic decision making when academic and social integration is limited. Decision trees identified course sequence rules to predict graduation, which can be used to formulate course pathways for community college advisors and their students. Results: Nine rules from the decision tree were identified, which could be used to advise community college students in coursetaking that aligns with career aspirations. The most important variable predicting graduation was completing College-Level Mathematics, which included Algebra II, Statistics, Precalculus, and survey mathematics courses. General education sciences courses such as Astronomy, Geology, Environmental Science, and Marine Biology were the most important science courses predicting graduation. Conclusions/Contributions: Results suggest the importance of College-Level Mathematics in providing the skills necessary for students to be successful in subsequent STEM coursework and persist to graduation. Designating specific academic pathways may improve social and academic integration and graduation rates, providing continuity as students work with different advisors to choose majors and plan course sequences. Transparent, accessible enrollment planning fosters programmatic consistency and student agency in selecting coursework that will maximize their success.
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作为社区学院数学补习生毕业预测因素的 STEM 入学决策树
目标/研究问题:社区大学的毕业率通常很低,而发展性数学的入学和选课模式可能会制约学术成果。为了找出提高社区大学毕业率的方法,我们利用决策树研究了 N = 5,065 名补习数学的学生的 STEM 课程选修模式。研究方法研究设计以 Tinto 的学术和社会融合框架为指导,该框架为确定决策树如何在学术和社会融合有限的情况下促进学术决策提供了分析视角。决策树确定了预测毕业的课程顺序规则,可用于为社区大学顾问及其学生制定课程路径。结果决策树确定了九条规则,可用于指导社区大学学生选修与职业理想相一致的课程。预测毕业的最重要变量是完成大学数学课程,其中包括代数 II、统计学、微积分预科和调查数学课程。天文学、地质学、环境科学和海洋生物学等通识教育科学课程是预测学生毕业的最重要科学课程。结论/贡献:研究结果表明,大学数学课程的重要性在于为学生提供必要的技能,帮助他们成功完成后续的科学、技术、工程和数学课程,并坚持到毕业。指定特定的学术路径可以提高社会和学术融合以及毕业率,在学生与不同的顾问合作选择专业和规划课程序列时提供连续性。透明、易懂的入学规划可以促进课程的一致性,让学生自主选择课程,最大限度地提高他们的成功率。
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来源期刊
Community College Review
Community College Review EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
2.40
自引率
7.70%
发文量
22
期刊介绍: The Community College Review (CCR) has led the nation for over 35 years in the publication of scholarly, peer-reviewed research and commentary on community colleges. CCR welcomes manuscripts dealing with all aspects of community college administration, education, and policy, both within the American higher education system as well as within the higher education systems of other countries that have similar tertiary institutions. All submitted manuscripts undergo a blind review. When manuscripts are not accepted for publication, we offer suggestions for how they might be revised. The ultimate intent is to further discourse about community colleges, their students, and the educators and administrators who work within these institutions.
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