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了解遗传性乳腺癌风险:智能辅导系统的处理位点

Understanding Genetic Breast Cancer Risk: Processing Loci of the Intelligent Tutoring System.

作者信息

Wolfe Christopher R, Reyna Valerie F, Widmer Colin L, Cedillos-Whynott Elizabeth M, Brust-Renck Priscila G, Weil Audrey M, Hu Xiangen

机构信息

Miami University, Oxford, OH.

Cornell University, Ithaca, NY.

出版信息

Learn Individ Differ. 2016 Jul;49:178-189. doi: 10.1016/j.lindif.2016.06.009. Epub 2016 Jul 1.

Abstract

The Intelligent Tutoring System helps women understand and make decisions about genetic testing for breast cancer risk. is guided by Fuzzy-Trace Theory, (FTT) and built using AutoTutor Lite. It responds differently to participants depending on what they say. Seven tutorial dialogues requiring explanation and argumentation are guided by three FTT concepts: forming gist explanations in one's own words, emphasizing decision-relevant information, and deliberating the consequences of decision alternatives. Participants were randomly assigned to , a control, or impoverished conditions removing gist explanation dialogues, argumentation dialogues, or FTT images. All conditions performed significantly better than controls on knowledge, comprehension, and risk assessment. Significant differences in knowledge, comprehension, and fine-grained dialogue analyses demonstrate the efficacy of gist explanation dialogues. FTT images significantly increased knowledge. Providing more elements in arguments against testing correlated with increased knowledge and comprehension.

摘要

智能辅导系统帮助女性理解乳腺癌风险基因检测并做出相关决策。该系统以模糊痕迹理论(FTT)为指导,使用AutoTutor Lite构建。它根据参与者的话语做出不同回应。七个需要解释和论证的辅导对话由FTT的三个概念指导:用自己的话形成要点解释、强调与决策相关的信息、思考决策选项的后果。参与者被随机分配到完整条件组、对照组或去除要点解释对话、论证对话或FTT图像的简化条件组。在知识、理解和风险评估方面,所有条件组的表现均显著优于对照组。知识、理解和细粒度对话分析中的显著差异证明了要点解释对话的有效性。FTT图像显著增加了知识。在反对检测的论证中提供更多要素与知识和理解的增加相关。

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