Optimizing input for language interventions
优化语言干预的输入
基本信息
- 批准号:9755400
- 负责人:
- 金额:$ 19.23万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-08-05 至 2021-07-31
- 项目状态:已结题
- 来源:
- 关键词:AdoptedAdultAlgorithmsBehaviorBeliefChildChildhoodCleaved cellComputer SimulationDataDemographic FactorsDevelopmentEarly InterventionEducational process of instructingEffectivenessEffectiveness of InterventionsFailureFutureGoalsHearingIndividualInstructionInterventionLanguageLanguage DevelopmentLanguage Development DisordersLanguage DisordersLawsLearningLinguisticsLiteratureMeasuresMemoryMeta-AnalysisMethodsModelingMorphologyOutcomeParentsPopulationPositioning AttributeRecommendationResearchResearch PersonnelSamplingSymptomsSystemSystems DevelopmentTestingTheoretical modelVariantWorkbasebehavioral studycognitive developmentdesigneffective interventionevidence basehigh riskkindergartenlanguage impairmentlearning outcomeliteracynovelpedagogyprediction algorithmpredictive modelingspecific language impairmentsyntaxteachertheoriestooltreatment programvirtual
项目摘要
Abstract
Children with language disorders are at high risk for academic failure, especially with respect to literacy.
Virtually all treatment programs focus on systematically manipulating language input from parents or clinicians.
The research presented in this proposal creates a theoretical framework for making detailed predictions
regarding what input will be most helpful for children. We build on models of pedagogical sampling, developed
in the cognitive development literature, in order to design input that is optimized for teaching language to
children. In this framework, teachers optimize data for statistical learners by selecting data that maximize
learners' belief in the correct hypothesis, relative to alternative hypotheses. We apply this pedagogical
sampling framework to interventions for children with specific language impairment (SLI) that are designed to
teach the productive use of grammatical morphemes. Aim 1 develops models of morphological and syntactic
learning with memory constraints; Aim 2 validates the learning models' ability to predict intervention outcomes;
and Aim 3 develops algorithms for predicting what new types of input data (e.g., specific sets of verbs) will be
most effective for teaching a child that a particular grammatical morpheme is productive. In the long term,
models derived in this project will facilitate the future development of systems that can give detailed
recommendations for input that would be most useful to children with a variety of language profiles.
摘要
有语言障碍的儿童学习失败的风险很高,特别是在识字方面。
几乎所有的治疗计划都集中在系统地处理父母或临床医生的语言输入。
这项提案中提出的研究为做出详细预测创造了一个理论框架
关于什么意见将对儿童最有帮助。我们建立在教学抽样的模型上,开发了
在认知发展文献中,为了设计为教学语言而优化的输入
孩子们。在这个框架中,教师通过选择最大化的数据来优化统计学习者的数据
学习者对正确假设的信念,相对于可选的假设。我们将这种教学方法应用于
针对特殊语言障碍(SLI)儿童的干预抽样框架,旨在
教授语法语素的有效用法。目标1开发词法和句法模型
记忆限制下的学习;目标2验证了学习模型预测干预结果的能力;
Aim 3开发了用于预测新的输入数据类型(例如,特定的动词集合)的算法
最有效的方法是教孩子一个特定的语法语素是有生产力的。从长远来看,
本项目中推导出的模型将有助于未来系统的开发,这些系统可以给出详细的
建议对具有各种语言特征的儿童最有用的输入。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
When Children's Production Deviates From Observed Input: Modeling the Variable Production of the English Past Tense.
当儿童的生产偏离观察到的输入时:对英语过去时的可变生产进行建模。
- DOI:10.1111/cogs.13328
- 发表时间:2023
- 期刊:
- 影响因子:2.5
- 作者:Barak,Libby;Harmon,Zara;Feldman,NaomiH;Edwards,Jan;Shafto,Patrick
- 通讯作者:Shafto,Patrick
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