首页> 外文会议>Frontiers in Education, 2002. FIE 2002. 32nd Annual >Nontraditional student withdrawal: a grounded Bayesian Vector Auto Regression framework
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Nontraditional student withdrawal: a grounded Bayesian Vector Auto Regression framework

机译:非传统的学生退学:扎根的贝叶斯向量自动回归框架

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Previously it has been proposed that explanations of nontraditional withdrawal might be defined by the underlying characteristics of the teaching and learning environment, especially on how a student's perceptions and expectations of that environment impact on their decision to withdraw. An ethnographic study using grounded theory was used to capture these underlying characteristics. The results of that study provided an explanation of the teaching and learning environment as a function of student beliefs, staff-student actions, and institutional intentions. A follow-up longitudinal study is now being undertaken. The aim of this study is, (a) refine the grounded analysis, and (b) model the grounded teaching and learning environment within a Bayesian vector auto regression (BVAR) framework.
机译:以前,有人提出非传统退学的解释可能由教与学环境的潜在特征来定义,特别是关于学生对环境的看法和期望如何影响其退学决定。使用基础理论的人种学研究被用来捕获这些潜在的特征。这项研究的结果根据学生的信念,教职工行为和机构意图对教学环境进行了解释。现在正在进行后续的纵向研究。这项研究的目的是:(a)完善扎根的分析,以及(b)在贝叶斯向量自回归(BVAR)框架内对扎根的教学环境进行建模。

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