Longitudinal Associations between Learning Assistants and Instructor Effectiveness
Longitudinal Associations between Learning Assistants and Instructor Effectiveness
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学习助理与教师效能之间的纵向关联
DOI：
10.1119/perc.2017.pr.015
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Van Dusen, Ben
中科院分区：
文献类型：
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作者：
Caravez, Daniel;De La Torre, Angelica;Nissen, Jayson M.;Van Dusen, Ben
学习助手（LA）模型的中心目标是通过教师实践的转变来提高学生的科学学习能力。关于大学物理教师经验对其有效性的影响的现有研究很少。为了调查大学物理入门教师在有或没有 LA 的情况下的经历与学生学习之间的关联，我们利用了 Learning About STEM Student Outcomes (LASSO) 数据库中的数据。 LASSO 数据库为我们提供了 4,365 名学生的学生级数据（概念清单分数和人口统计数据）以及学生 93 门力学课程的课程级数据（教师经验和课程特色）。在对教师经验和学生学习之间的关联进行建模时，我们使用分层多重插补来插补缺失的数据，并使用分层线性建模将学生嵌套在课程中。我们的模型预测，随着教师在没有 LA 的情况下获得教学经验，他们的效率会下降。然而，洛杉矶支持的环境似乎可以弥补这种有效性下降，因为教师在获得洛杉矶教学经验的同时保持了有效性。
A central goal of the Learning Assistant (LA) model is to improve students' learning of science through the transformation of instructor practices. There is minimal existing research on the impact of college physics instructor experiences on their effectiveness. To investigate the association between college introductory physics instructors' experiences with and without LAs and student learning, we drew on data from the Learning About STEM Student Outcomes (LASSO) database. The LASSO database provided us with student-level data (concept inventory scores and demographic data) for 4,365 students and course-level data (instructor experience and course features) for the students' 93 mechanics courses. We performed Hierarchical Multiple Imputation to impute missing data and Hierarchical Linear Modeling to nest students within courses when modeling the associations between instructor experience and student learning. Our models predict that instructors' effectiveness decreases as they gain experience teaching without LAs. However, LA supported environments appear to remediate this decline in effectiveness as instructor effectiveness is maintained while they gain experience teaching with LAs.