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Prediction of retention and probation status of first-year college students in learning communities using binary logistic regression models.

机译:使用二元逻辑回归模型预测学习社区中一年级大学生的保留和试用状态。

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摘要

The first year of college is a critical period of transition for incoming college students. Learning communities have been identified as an approach to link students together in courses that are intentionally integrated and designed with first-year students' needs in mind. Yet, learning community teaching teams are often not provided with data prior to the start of the semester about their students in order to target interventions. Also, it remains unclear as to which students are most benefitted by participating in learning communities. One question then becomes, what variables known on or before the first day of classes are predictive of first-year student success, in terms of retention and probation status, for first-year college students in learning communities?;The correlational study employed univariate and multivariate analyses on pre-college data about three consecutive cohorts of first-year students in learning communities at a regional public university in South Texas. Logistic regression models were developed to predict retention and probation status without respect to learning community membership, as well as for each learning community category.;Results indicated that group differences were not statistically significant based on either first-generation status or age for retention, while group differences were statistically significant for probation status on the basis of all of the pre-college variables except age. Although statistically significant differences were found among the learning community categories for each of the pre-college variables, there were no statistically significant group differences in their retention or probation rates.;The model to predict retention regardless of learning community membership included five variables, while the model to predict probation status included eight variables. The models for each learning community contained different sets of predictor variables; the most common predictors of retention or probation status were high school percentile and orientation date.;The study has practical implications for admissions officers, orientation planners, student support services, and learning community practitioners. It is recommended to replicate the study with more recent learning community cohorts and additional pre-college variables, as well as in programs across the nation, to contribute to the literature about the potential for learning communities to enhance first-year student success.
机译:大学第一年是即将到来的大学生过渡的关键时期。学习社区已被确定为一种将学生联系在一起的方法,这些课程是根据一年级学生的需求有意整合和设计的。然而,在学期开始之前,学习社区的教学团队通常不会获得有关其学生的数据,以便进行干预。而且,尚不清楚哪个学生通过参加学习社区而受益最大。那么一个问题就变成了,在学习社区的一年级大学生中,在上课第一天或之前知道的哪些变量可以预测一年级学生的成功率(在保留率和缓刑状态方面)?对南得克萨斯州一所地区公立大学学习社区中三个连续的一年级学生的大学前数据进行多元分析。发展了Logistic回归模型来预测保留和缓刑状态,而无需考虑学习社区成员身份以及每个学习社区类别的影响;结果表明,基于第一代状态或保留年龄,群体差异在统计学上不显着,而根据大学前所有变量(年龄除外),两组的缓刑状态差异在统计学上具有统计学意义。尽管在每个大学前变量的学习社区类别之间发现了统计学上的显着差异,但在保留或试用率方面没有统计学上显着的群体差异。;无论学习社区成员身份如何,预测保留率的模型都包括五个变量,而预测缓刑状态的模型包括八个变量。每个学习社区的模型包含不同的预测变量集。保留或缓刑状态最常见的预测指标是高中百分位数和入学日期。该研究对招生官,入学计划者,学生支持服务和学习社区从业人员具有实际意义。建议将研究与最近的学习社区队列和更多的大学预科变量一起复制,并在全国范围内的计划中进行复制,以为有关学习社区提高第一年级学生成功的潜力的文献做出贡献。

著录项

  • 作者

    Sperry, Rita A.;

  • 作者单位

    Texas A&M University - Corpus Christi.;

  • 授予单位 Texas A&M University - Corpus Christi.;
  • 学科 Higher education.;Adult education.;Educational administration.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 114 p.
  • 总页数 114
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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