Characterizing the most effective scaffolding approaches in engineering and technology education: A clustering approach
Characterizing the most effective scaffolding approaches in engineering and technology education: A clustering approach
复制标题
表征工程和技术教育中最有效的脚手架方法：聚类方法
DOI：
10.1002/cae.22556
复制
发表时间：
2022
影响因子：
2.9
通讯作者：
Kim, ChanMin
中科院分区：
文献类型：
--
作者：
Belland, Brian R.;Lee, Eunseo;Zhang, Anna Y.;Kim, ChanMin
这项研究表明，在计算机科学和技术教育设置的脚手架功能的最有效的组合。它解决了研究问题，“什么样的脚手架特征，使用环境和评估水平的组合导致大学和研究生水平的工程和技术学习者中的中等和大的效果大小？”为此，在支架Meta分析数据集中确定了在技术和工程教育背景下支架导致中等或较大效应量的研究。接下来，使用SPSS 24中的两步聚类分析来识别为本科生和研究生学习计算机科学而定制的不同支架属性组。输入变量包括不同的支架特征，使用的背景下，教育水平和效果大小。有一个八簇的解决方案：五个簇与大效应量相关，两个与中等效应量相关，一个与中等和大效应量相关。三个最重要的预测因素是使用scaffolding的上下文，scaffolding是否以及如何随着时间的推移进行定制，以及管理scaffolding变化的决策规则。值得注意的是，高效的脚手架集群与每个预测因子的大多数水平相关。
This study indicates the most effective combinations of scaffolding features within computer science and technology education settings. It addresses the research question, “What combinations of scaffolding characteristics, contexts of use, and assessment levels lead to medium and large effect sizes among college‐ and graduate‐level engineering and technology learners?” To do so, studies in which scaffolding led to a medium or large effect size within the context of technology and engineering education were identified within a scaffolding meta‐analysis data set. Next, two‐step cluster analysis in SPSS 24 was used to identify distinct groups of scaffolding attributes tailored to learning computer science at the undergraduate and graduate levels. Input variables included different scaffolding characteristics, the context of use, education level, and effect size. There was an eight‐cluster solution: five clusters were associated with large effect size, two with medium effect size, and one with both medium and large effect size. The three most important predictors were the context in which scaffolding was used, if and how scaffolding is customized over time and the decision rules that govern scaffolding change. Notably, highly effective scaffolding clusters are associated with most levels of each predictor.
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