Civic learning outcomes: a step towards an inclusive higher education     

Diana Dias 《International Journal of Inclusive Education》2018,22(4):360-374

An inclusive education goes beyond the acquisition of discipline knowledge or skills. Inclusion is concerned with the participation and integration of all students (regardless of their intrinsic characteristics), helping them to develop civic competences. Civic and democratic values, equality and social justice became critical dimensions in this broader concept of education. This paper argues that the incorporation of civic dimensions, such as civic knowledge, civic skills or civic values in academic curricula could be an effective step towards more inclusive education. Specifically, this work intends to explore what civic dimensions are emphasised as a learning outcome in Portuguese higher education programmes. Adopting a qualitative methodology, typologies and incidence of civic learning outcomes were analysed and compared across three academic levels (first, second and third study cycles). The results provide a better understanding of what civic dimensions are stressed by institutions. All types of civic learning outcomes have been reinforced, defining civic values, civic skills and civic knowledge as expectable learning results. Both civic values and skills are well represented while civic knowledge is the less mentioned category. The enforcement of such civic dimensions is a valuable approach to enhancing education as a collective societal endeavour and as a common good.  相似文献   

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.

  相似文献   

Videodisc/Microcomputer Courseware Design,edited by Michael L. DeBloois. Educational Technology Publications,Englewood Cliffs,New Jersey, 1982, 178 pp., $24.95 cloth,ISBN 0-87778-183-4     

E. Travis Hicks 《Performance Improvement》1983,22(9):52-52

  相似文献   

School climate in Mongolia: Translation and validation of the What’s Happening in This School     

Sattler  David N.  Gruman  Diana H.  Enkhtur  Oyundelger  Muskavage  Brett  Bishkhorloo  Boldsuren 《Learning Environments Research》2022,25(2):325-340

Learning Environments Research - School climate measures are important tools that assist educators in evaluating the “norms, values, and expectations that support people feeling socially,...  相似文献