视觉空间能力和学生的学习方法是人体解剖学书面评估和实验室评估中学习成绩的预测因素。

IF 5.2 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES
Catherine Wang, Sean C. McWatt
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引用次数: 1

摘要

随着专门用于人体解剖学课程的时间受到威胁,课程提供方法也在不断变化,许多新的教学创新正在出现,需要进行全面评估,以确保循证教学得以维持。尽管成绩是衡量“学习”的主要指标,但其他指标可以评估更细微、更有意义的结果。学生对身体和学习深度的三维理解的两个常见预测因素分别是视觉空间能力和学习方法。这项研究评估并比较了这些指标在人体解剖学课程中的书面评估和实验室评估中的相对预测能力。深度学习方法和视觉空间能力预计与整体表现呈正相关,视觉空间能力是更显著的预测因素,尤其是在实验室评估中。此外,视觉空间能力预计与深度学习方法呈正相关,与表面学习方法负相关。控制协变量的多元线性回归模型发现,视觉空间能力(p = 0.049;p = 0.014)和深度学习方法(p = 0.001;p = 0.001)是期末成绩和实验室成绩的独立显著预测因素,而只有深度学习方法才能显著预测书面成绩(p = 0.007)。视觉空间能力与学习方法之间没有显著关系。鉴于这些发现以及解剖学对视觉空间要求高的数字学习活动的日益依赖,在评估教学创新的影响时,应考虑这两个指标。此外,教育工作者应该设计学习资源和环境,以培养学生的视觉空间能力,并推广更深层次的学习方法,最大限度地提高学生的成功率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Visuospatial ability and student approach to learning as predictors of academic performance on written versus laboratory-based assessments in human anatomy

Visuospatial ability and student approach to learning as predictors of academic performance on written versus laboratory-based assessments in human anatomy
As hours devoted to human anatomy curricula fall under threat and curricular delivery methods remain in flux, many new teaching innovations are emerging, which require comprehensive evaluation to ensure evidence‐based teaching is maintained. Although grades are the predominant measure of ‘learning’, alternative metrics can assess more nuanced and meaningful outcomes. Two common predictors of students' three‐dimensional understanding of the body and depth of learning are visuospatial abilities and approaches to learning, respectively. This study evaluated and compared the relative predictive power of these metrics on written and laboratory‐based assessments in a human anatomy course. Deep approaches to learning and visuospatial abilities were expected to positively correlate with overall performance, with visuospatial abilities being the more salient predictor, especially on laboratory‐based assessments. Additionally, visuospatial abilities were expected to positively correlate with deep learning approaches and negatively correlate with surface learning approaches. Multiple linear regression models controlling for covariates found that both visuospatial abilities (p = 0.049; p = 0.014) and deep learning approaches (p = 0.001; p = 0.001) were independent significant predictors of final and laboratory‐based grades, while only deep learning approaches were significantly predictive of written grades (p = 0.007). There was no significant relationship between visuospatial abilities and approaches to learning. Given these findings and the increased reliance on visuospatially demanding digital learning activities in anatomy, both metrics should be considered when evaluating the impact of teaching innovations. Further, educators should design learning resources and environments that train visuospatial abilities and promote deeper learning approaches to maximize students' success.
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来源期刊
Anatomical Sciences Education
Anatomical Sciences Education Anatomy/education-
CiteScore
10.30
自引率
39.70%
发文量
91
期刊介绍: Anatomical Sciences Education, affiliated with the American Association for Anatomy, serves as an international platform for sharing ideas, innovations, and research related to education in anatomical sciences. Covering gross anatomy, embryology, histology, and neurosciences, the journal addresses education at various levels, including undergraduate, graduate, post-graduate, allied health, medical (both allopathic and osteopathic), and dental. It fosters collaboration and discussion in the field of anatomical sciences education.
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