Using causal models to bridge the divide between big data and educational theory     

Kirsty Kitto  Ben Hicks  Simon Buckingham Shum 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1095-1124

An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.

Practitioner notes

What is already known about this topic

• ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
• Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
• Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.

What this paper adds

• An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
• A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
• An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.

Implications for practice and/or policy

• Causal models can help us to explicitly specify educational theories in a testable format.
• It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
• Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.

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微积分的内核裂变与“后微积分范式”的数学教育价值     

黄秦安 《数学教育学报》2022,(1)

微积分理论作为人类历史上伟大的知识创造之一,自诞生之后在相当长一段时期内被奉为描绘宇宙与自然运行强有力的数学语言与模型.20世纪以来,作为具有典型革命性意义的知识创新,诞生了分形几何学、混沌理论和复杂性科学等多种新兴学科.这些重要的数学知识创造构成了后微积分时代的主流数学知识形态并凝聚成为一种新的数学范式——“后微积分范式”.作为微积分范式的一种内核裂变,它实现了对原有范式的颠覆、突破和迁越,具有非确定性、混沌性和复杂性等显著的当代科学革命特征.“后微积分范式”已经构成了大学数学课程的重要组成部分和必要内容,也必将成为未来高中甚至义务教育数学课程的基本内容.因此,“后微积分范式”的数学教育意义以及如何开展教学的话题需要予以充分的论证和关注.  相似文献   

Visual expression of factor decomposition in regression analysis: An example of Japanese housing rents     

Kosei Fukuda 《Teaching Statistics》2023,45(2):100-105

This paper presents the importance of the visual expression of factor decomposition in regression analysis, which is particularly worthwhile for undergraduate students whose majors are not mathematics but social science. The conventional purpose of regression analysis is to examine specific hypotheses empirically. In particular, the statistical significance of the explanatory variable was tested, which may have been difficult for many students to understand mathematically. To remedy this, factor decomposition is introduced in the same way that human body composition is broken down into water, fat, and muscle. As an illustrative example, multiple regression was applied to the determinants of housing rents in Japan. The explanatory variables were the living area, building age, and walking time from the nearest station. The findings suggest that, with the help of visual expression, a student can easily appreciate which variable significantly affects housing rents.  相似文献   

工科研究型大学人文社科学部构建研究     

钱晓辉 《江苏高教》2022,(2):70-73

随着"双一流"建设的推进,为激发高校学术组织创新活力,中国部分高校逐渐进行学部制改革。学部作为学术分类管理的平台,对指导学科分类、深入民主管理具有重要作用。面对中国工科研究型大学"工强文弱"的状况,应通过构建人文社科学部来引导和推动人文社科建设,但在学部建设过程中要注意克服发展定位不准、有效融合不够、院部职能协调不清等问题,要对学部进行有效定位,重视其运行模式和组织架构设计。  相似文献   

未被期待的德国应用科学大学何以成功 ——基于历史的考察          下载免费PDF全文

唐柳 《复旦教育论坛》2022,20(6):96-104

加强应用型本科高校建设，是我国高等教育普及化阶段的重要任务。放眼国际，德国应用科学大学在创建之初就迅猛发展，并逐渐成为德国高等教育的重要支柱。本文从新制度主义视角回看德国应用科学大学的产生与发展，发现举办应用科学大学并不是德国高等教育改革的首要举措，也并非当时最被看好的举措。其成功兼具偶然性和必然性，并经历了改革酝酿期、博弈运行期、规范发展期以及战略调整期。未被期待的德国应用科学大学能在制度博弈中脱颖而出，得益于以制度改革积极回应社会诉求、以错位发展将特色转化为优势、以规模速增自成体系、拥有开放包容的发展环境。  相似文献