荷兰九年级学生求学动机分析

Q4 Mathematics
Denise M. Blom, M. Warrens, Meike Faber
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引用次数: 0

摘要

摘要本研究的目的是在四维动机空间中识别荷兰九年级学生的学习动机特征,包括掌握、表现、社会动机和外在动机。采用多聚类方法(K-means、k - medidoids、限制性潜在剖面分析)和多指标选择最优聚类数量。统计选择方法在最优聚类数量上并不完全一致,但Calinski-Harabasz指数和剪影值的最小值和平均值提供了一个明确的公分母。无论采用何种聚类方法,这三个指标都表明两个聚类是最优数量:一个聚类是所有维度平均得分高的九年级学生,另一个聚类是所有维度平均得分低的九年级学生。此外,我们还探讨了多集群解决方案的实质性解释。研究发现,大多数学生所在的集群可以被划分为三种不同水平的概况类型之一:(1)所有维度的平均得分大致相等,(2)掌握和社会动机的平均得分相对较高,(3)绩效动机的平均得分相对较低。后一种剖面类型被认为是新发现的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
School motivation profiles of Dutch 9th graders
Abstract The aim of this study was to identify school motivation profiles of Dutch 9th grade students in a four-dimensional motivation space, including mastery, performance, social and extrinsic motivation. Multiple clustering methods (K-means, K-medoids, restricted latent profile analysis) and multiple indices for selecting the optimal number of clusters were applied. The statistical selection methods did not completely concur on the optimal number of clusters, but a clear common denominator was provided by the Calinski-Harabasz index and the minimum and mean Silhouette values. All three indices indicated two clusters as the optimal number, regardless of the clustering method used: one cluster of 9th graders with high average scores on all dimensions and one cluster with low mean scores on all dimensions. In addition, we explored the substantive interpretation of multiple cluster solutions. It was discovered that most students are in clusters that can be classified into one of three profile types that may differ in level: (1) approximately equal mean scores on all dimensions, (2) relative high mean scores on mastery and social motivation, and (3) a relatively low mean score on performance motivation. The latter profile type is believed to be a new discovery.
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CiteScore
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