学习障碍和困难:从分类视角到维度视角

IF 3.8 1区 心理学 Q1 PSYCHOLOGY, EDUCATIONAL Learning and Individual Differences Pub Date : 2024-06-19 DOI:10.1016/j.lindif.2024.102490
Sara Caviola , Samuel Greiff , Enrico Toffalini
{"title":"学习障碍和困难:从分类视角到维度视角","authors":"Sara Caviola ,&nbsp;Samuel Greiff ,&nbsp;Enrico Toffalini","doi":"10.1016/j.lindif.2024.102490","DOIUrl":null,"url":null,"abstract":"<div><p>According to the emerging dimensional framework, most neurodevelopmental disorders may be conceptualised as extreme ends of developmental continua that span through the entire population (e.g., Astle et al., 2022; Peters &amp; Ansari, 2019). This framework describes not only learning difficulties, but potentially most neurodiversity as the result of individuals being distributed along a manifold of variously correlated and continuous dimensions, that span from neurotypicality to neurodivergence in a largely seamless way. In this, a heterogeneous range of conditions may easily be reframed as part of the general variability in the population, rather than as segmented subpopulations with qualitatively different features. In the present editorial, we discuss this framework with reference to the field of learning disorders and difficulties. We will repeatedly refer to the suggestions made by Astle et al. (2022) in their review on the “transdiagnostic revolution” of neurodevelopmental disorders. The research program that they advocate has two methodological tenets: investigating underlying continuous dimensions (dimensional framework), and exploring clustering (with an eye to potentially developing new data-driven taxonomies). Here, we mainly endorse adopting a dimensional framework, at least in the field of learning disorders, while we raise some cautionary notes on the risks of clustering. We also discuss open issues related to recruiting participants, improving psychometrics tools, and discovering cognitive and non-cognitive correlates of conditions when it comes to studying learning difficulties and learning disorders.</p></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"113 ","pages":"Article 102490"},"PeriodicalIF":3.8000,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Learning disorders and difficulties: From a categorical to a dimensional perspective\",\"authors\":\"Sara Caviola ,&nbsp;Samuel Greiff ,&nbsp;Enrico Toffalini\",\"doi\":\"10.1016/j.lindif.2024.102490\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>According to the emerging dimensional framework, most neurodevelopmental disorders may be conceptualised as extreme ends of developmental continua that span through the entire population (e.g., Astle et al., 2022; Peters &amp; Ansari, 2019). This framework describes not only learning difficulties, but potentially most neurodiversity as the result of individuals being distributed along a manifold of variously correlated and continuous dimensions, that span from neurotypicality to neurodivergence in a largely seamless way. In this, a heterogeneous range of conditions may easily be reframed as part of the general variability in the population, rather than as segmented subpopulations with qualitatively different features. In the present editorial, we discuss this framework with reference to the field of learning disorders and difficulties. We will repeatedly refer to the suggestions made by Astle et al. (2022) in their review on the “transdiagnostic revolution” of neurodevelopmental disorders. The research program that they advocate has two methodological tenets: investigating underlying continuous dimensions (dimensional framework), and exploring clustering (with an eye to potentially developing new data-driven taxonomies). Here, we mainly endorse adopting a dimensional framework, at least in the field of learning disorders, while we raise some cautionary notes on the risks of clustering. We also discuss open issues related to recruiting participants, improving psychometrics tools, and discovering cognitive and non-cognitive correlates of conditions when it comes to studying learning difficulties and learning disorders.</p></div>\",\"PeriodicalId\":48336,\"journal\":{\"name\":\"Learning and Individual Differences\",\"volume\":\"113 \",\"pages\":\"Article 102490\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Learning and Individual Differences\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1041608024000839\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, EDUCATIONAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Learning and Individual Differences","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1041608024000839","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EDUCATIONAL","Score":null,"Total":0}
引用次数: 0

摘要

根据新兴的维度框架,大多数神经发育障碍可被概念化为横跨整个人群的发育连续性的极端端点(例如,Astle et al.)这一框架不仅描述了学习困难,还可能将大多数神经多样性描述为个体沿着各种相关的连续维度分布的结果,这些维度以一种基本无缝的方式从神经典型性跨越到神经分化。因此,各种不同的情况很容易被重新定义为人群中普遍变异性的一部分,而不是具有不同特征的细分亚群。在本社论中,我们将结合学习障碍和学习困难领域来讨论这一框架。我们将反复提及 Astle 等人(2022 年)在其关于神经发育障碍的 "跨诊断革命 "综述中提出的建议。他们所倡导的研究计划有两个方法论原则:研究潜在的连续维度(维度框架)和探索聚类(着眼于可能开发新的数据驱动分类法)。在此,我们主要赞同采用维度框架,至少在学习障碍领域是如此,同时我们也对聚类的风险提出了一些警示。我们还讨论了在研究学习困难和学习障碍时,与招募参与者、改进心理测量工具以及发现认知和非认知相关条件有关的开放性问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Learning disorders and difficulties: From a categorical to a dimensional perspective

According to the emerging dimensional framework, most neurodevelopmental disorders may be conceptualised as extreme ends of developmental continua that span through the entire population (e.g., Astle et al., 2022; Peters & Ansari, 2019). This framework describes not only learning difficulties, but potentially most neurodiversity as the result of individuals being distributed along a manifold of variously correlated and continuous dimensions, that span from neurotypicality to neurodivergence in a largely seamless way. In this, a heterogeneous range of conditions may easily be reframed as part of the general variability in the population, rather than as segmented subpopulations with qualitatively different features. In the present editorial, we discuss this framework with reference to the field of learning disorders and difficulties. We will repeatedly refer to the suggestions made by Astle et al. (2022) in their review on the “transdiagnostic revolution” of neurodevelopmental disorders. The research program that they advocate has two methodological tenets: investigating underlying continuous dimensions (dimensional framework), and exploring clustering (with an eye to potentially developing new data-driven taxonomies). Here, we mainly endorse adopting a dimensional framework, at least in the field of learning disorders, while we raise some cautionary notes on the risks of clustering. We also discuss open issues related to recruiting participants, improving psychometrics tools, and discovering cognitive and non-cognitive correlates of conditions when it comes to studying learning difficulties and learning disorders.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Learning and Individual Differences
Learning and Individual Differences PSYCHOLOGY, EDUCATIONAL-
CiteScore
6.60
自引率
2.80%
发文量
86
期刊介绍: Learning and Individual Differences is a research journal devoted to publishing articles of individual differences as they relate to learning within an educational context. The Journal focuses on original empirical studies of high theoretical and methodological rigor that that make a substantial scientific contribution. Learning and Individual Differences publishes original research. Manuscripts should be no longer than 7500 words of primary text (not including tables, figures, references).
期刊最新文献
The structure of adult thinking: A network approach to (meta)cognitive processing Ink and pixels: Impact of highlighting and reading self-efficacy on adolescents' cognitive load, epistemic emotions, and text comprehension Students' study activities before and after exam deadlines as predictors of performance in STEM courses: A multi-source data analysis The relationship between positive and painful emotions and cognitive load during an algebra learning task Idiographic learning analytics: Mapping of the ethical issues
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1