Impact of word properties on list learning: An explanatory item analysis.
Impact of word properties on list learning: An explanatory item analysis.
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单词属性对列表学习的影响：解释性项目分析。
DOI：
10.1037/neu0000810
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发表时间：
2023
期刊：
影响因子：
2.4
通讯作者：
Cullum,CMunro
中科院分区：
文献类型：
--
作者：
Goette,WilliamF;Schaffert,Jeff;Carlew,Anne;Rossetti,Heidi;Lacritz,LauraH;DeBoeck,Paul;Cullum,CMunro
目的：影响词汇学习成绩的因素多种多样，而词汇选择对词汇学习成绩影响的研究相对较少。本研究探讨单字的语言加工和记忆加工对表学习的影响。方法采用统一认知评估方案中1219名年龄= 74.41 (SD= 7.13)，教育程度= 13.30 （SD= 2.72）的参与者的项目反应数据。采用贝叶斯广义（非线性）多层次建模框架来确定测量和解释项目反应理论模型。通过实验学习、单词序列位置和英语词典项目获得的六个单词属性对项目的解释效应进行了建模。结果双参数logistic （2PL）模型具有试验特异性学习效果，测量拟合效果最佳。观察到序列位置效应对单词学习的证据。身体-客体整合对词汇学习有显著的正向影响，而词频、具体性和语义多样性对词汇学习有显著的负向影响。平均习得年龄对词汇产生微弱的消极影响，而单词中音素的数量对词汇产生微弱的积极影响。结论：表学习成绩与单词重复次数以外的因素有关。预测学习的项目因素的识别可以扩展到一系列的测试开发问题，包括翻译、形式等同、项目修订和项目偏见。在数据协调工作中，这些方法还可用于通过共享项目特征来帮助链接测试，并测试这些特征在不同样本中是否具有相同的解释性。
ObjectiveA variety of factors affect list learning performance and relatively few studies have examined the impact of word selection on these tests. This study examines the effect of both language and memory processing of individual words on list learning.MethodItem-response data from 1,219 participants, M age= 74.41 (SD= 7.13), M edu= 13.30 (SD= 2.72), in the Harmonized Cognitive Assessment Protocol were used. A Bayesian generalized (non) linear multilevel modeling framework was used to specify the measurement and explanatory item-response theory models. Explanatory effects on items due to learning over trials, serial position of words, and six word properties obtained through the English Lexicon Project were modeled.ResultsA two parameter logistic (2PL) model with trial-specific learning effects produced the best measurement fit. Evidence of the serial position effect on word learning was observed. Robust positive effects on word learning were observed for body-object integration while robust negative effects were observed for word frequency, concreteness, and semantic diversity. A weak negative effect of average age of acquisition and a weak positive effect for the number of phonemes in the word were also observed.ConclusionsResults demonstrate that list learning performance depends on factors beyond the repetition of words. Identification of item factors that predict learning could extend to a range of test development problems including translation, form equating, item revision, and item bias. In data harmonization efforts, these methods can also be used to help link tests via shared item features and testing of whether these features are equally explanatory across samples.
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DOI：
10.1016/0090-1229(84)90193-4
发表时间：
1984
期刊：
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影响因子：
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DOI：
10.1126/science.6171885
发表时间：
1982
期刊：
Science (New York, N.Y.)
影响因子：
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作者：
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10.1016/s0021-9258(17)38981-0
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