非层次复杂性变量对性能的小影响。

M. Commons, Sagun Giri, W. Harrigan
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引用次数: 5

摘要

即使结果显示解决问题的大部分困难都是由项目的层次复杂性来解释的,仍然有一些变量在很小的程度上帮助预测项目衡量困难的程度。人们必须了解这些变量是什么,并在分析来自旨在衡量项目层次复杂性顺序影响的工具的数据时考虑到它们。本研究旨在检验小变量对任务表现的影响。测试的变量包括层次复杂性、位置顺序、需要计算的数量、数字的大小和因果变量的位置。参与者被要求从逻辑/数学/物理科学子领域的任务序列中解决问题。使用的四种仪器是代数、平衡木、无限和洗衣仪器。这些工具是基于层次复杂性(MHC)模型。参与者被要求先完成洗衣任务序列,然后进入下一个任务序列。每个仪器的项目分别进行分析和分组分析。对所有仪器的所有项目进行了拉希分析。被认为有影响的变量被编码。然后使用逐步回归分析编码变量。采用逐步回归,小变量以和不以层次复杂性为因素进行测试。根据项目的阶段分数对变量进行了回归。以层次复杂性为变量之一的逐步回归占方差的95%左右,β大于0.9。除层次复杂度外,其余变量的逐步回归方差和β值相对较低。结果表明,层次复杂度的顺序具有很强的预测作用,可以解释大部分的方差。其他变量的贡献很小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The small effects of non-hierarchical complexity variables on performance.
Even when the results show that most of the difficulty in solving problems is explained by the hierarchical complexity of the item, there are still variables that help in small ways in predicting how well items measure difficulty. One must understand what these variables are and take them into consideration when analyzing data from instruments designed to measure the impact of the order of hierarchical complexity of items. This study was designed to test the effect of small variables on task performance. The variables tested were hierarchical complexity, place in order, the number of calculations needs, the size of the numbers, and the causal variable position. Participants were asked to solve problems from task sequences from the logic/mathematics/physical science subdomains. The four instruments used were the algebra, balance beam, infinity and laundry instruments. These instruments were based on the model of hierarchical complexity (MHC). Participants were asked to first complete the laundry task sequence and move to the next task sequence. Items from each instrument were analyzed individually and as a group. A Rasch analysis was performed on all the items from all the instruments. The variables thought to have an effect were coded. The coded variables were then analyzed using stepwise regression. A stepwise regression was used and the small variables were tested with and without hierarchical complexity as a factor. The variables were regressed against the stage score of the items. For all four instruments stepwise regression with hierarchical complexity as one of the variable accounted for about 95% of the variance and the β was greater than 0.9. Stepwise regression with all the other variables except hierarchical complexity accounted for relatively lower variance and β. The results showed that order of hierarchical complexity has a very strong predictive role and accounts for most of the variance. The other variables only made very small contributions.
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