[论文解读] Quantifying the relationship between student enrollment patterns and student performance

Shahab Boumi, Adan Vela+1 more|arXiv (Cornell University)|Mar 22, 2020

Higher Education Research Studies参考文献 44被引用 7

一句话总结

本研究利用隐马尔可夫模型（HMM）基于中佛罗里达大学（2008–2017年）的多学期注册数据，对学生进行长期注册策略分类——全时、半时或混合。研究发现，混合注册学生（尤其是首次入 college 的学生和转学生）在学术表现和毕业率方面显著优于半时注册学生，且在转为全时学期后GPA有所提升。

ABSTRACT

Simplified categorizations have often led to college students being labeled as full-time or part-time students. However, at many universities student enrollment patterns can be much more complicated, as it is not uncommon for students to alternate between full-time and part-time enrollment each semester based on finances, scheduling, or family needs. While prior research has established full-time students maintain better outcomes then their part-time counterparts, limited study has examined the impact of enrollment patterns or strategies on academic outcomes. In this paper, we applying a Hidden Markov Model to identify and cluster students' enrollment strategies into three different categorizes: full-time, part-time, and mixed-enrollment strategies. Based the enrollment strategies we investigate and compare the academic performance outcomes of each group, taking into account differences between first-time-in-college students and transfer students. Analysis of data collected from the University of Central Florida from 2008 to 2017 indicates that first-time-in-college students that apply a mixed enrollment strategy are closer in performance to full-time students, as compared to part-time students. More importantly, during their part-time semesters, mixed-enrollment students significantly outperform part-time students. Similarly, analysis of transfer students shows that a mixed-enrollment strategy is correlated a similar graduation rates as the full-time enrollment strategy, and more than double the graduation rate associated with part-time enrollment. Such a finding suggests that increased engagement through the occasional full-time enrollment leads to better overall outcomes.

研究动机与目标

• 通过使用多学期数据，超越简单的全时/半时分类，识别学生的长期注册策略。
• 探究不同注册策略（全时、半时或混合）对学术表现和学业持续性结果的影响。
• 比较首次入 college（FTIC）学生与转学生在不同注册策略下的表现差异。
• 评估注册策略转换对GPA和辍学风险的影响，特别是从半时转为全时注册的影响。
• 通过识别易受伤害的学生群体和有效的注册模式，为机构支持提供依据。

提出的方法

• 应用隐马尔可夫模型（HMM）基于学期间的学分注册数据，将学生聚类为长期注册策略。
• 使用HMM识别出三种不同的注册策略：全时注册策略（FES）、半时注册策略（PES）和混合注册策略（MES）。
• 根据学生在多个学期中的注册模式进行分类，而非仅依据单学期状态。
• 采用t检验和双因素方差分析（ANOVA）评估注册策略转换对GPA和辍学率的影响。
• 分析不同注册策略群体的GPA、DFW（D、F或退课）率和毕业率，按FTIC和转学生身份分层。
• 使用统计建模评估注册策略和注册学院对学术表现结果的显著性影响。

实验结果

研究问题

• RQ1 长期注册策略（全时、半时、混合）与学术表现和学业持续性结果之间有何关联？
• RQ2 首次入 college 的学生若采用混合注册策略，其表现是否更接近全时学生而非半时学生？
• RQ3 采用混合注册策略的转学生是否达到与全时学生相当的毕业率，且显著高于半时学生？
• RQ4 从半时注册转为全时注册策略对GPA和辍学风险有何影响？
• RQ5 不同注册策略群体之间的家庭收入分布有何差异？这对学生支持有何启示？

主要发现

• 采用混合注册策略（MES）的首次入 college（FTIC）学生，其学术表现更接近全时学生而非半时学生。
• 在半时注册学期中，混合注册的FTIC学生显著优于持续半时注册的学生，表明其参与度或抗逆力更高。
• 采用混合注册策略的转学生毕业率与全时学生相当，且是半时学生的两倍以上。
• 从半时注册转为全时注册策略，对FTIC和转学生而言，GPA均出现统计上显著的提升，且辍学风险下降。
• 当学生从全时转为半时注册时，辍学概率上升；而从半时转为全时注册则降低辍学风险。
• 基于HMM的分类成功识别出三种不同的注册策略，揭示长期注册模式比单学期状态更能预测学业结果。