‘Getting the pieces together’: negotiating the transition from pre-service to in-service teacher

For beginning teachers to make the transition to full professional membership they need to increase their professional knowledge of the art and science of teaching. This paper explores the difference in knowledge growth between beginning teachers who commence teaching in fragmented teaching situations in the first two years of teaching, and their colleagues who have stable, secure and continuing employment during this time. This paper argues that the employment context in which beginning teachers take up their profession has a significant, but hitherto largely unacknowledged, effect on the capacity of teachers to develop the craft of teaching (Elbaz, 1983 Elbaz, F. 1983. Teacher thinking: A study of practical knowledge, London: Croom Helm.  [Google Scholar]); on their continuing commitment to the profession; and on their self-confidence and self-image as teachers It is concluded that long-term, secure employment in one school, with full responsibility for a class of students and access to effective mentoring, is necessary if beginning teachers are to move beyond ‘survival’ to developing competency in the first two years of teaching.     

The social life of Learning Analytics: cluster analysis and the ‘performance’ of algorithmic education

This paper argues that methods used for the classification and measurement of online education are not neutral and objective, but involved in the creation of the educational realities they claim to measure. In particular, the paper draws on material semiotics to examine cluster analysis as a ‘performative device’ that, to a significant extent, creates the educational entities it claims to objectively represent through the emerging body of knowledge of Learning Analytics (LA). It also offers a more critical and political reading of the algorithmic assemblages of LA, of which cluster analysis is a part. Our argument is that if we want to understand how algorithmic processes and techniques like cluster analysis function as performative devices, then we need methodological sensibilities that consider critically both their political dimensions and their technical-mathematical mechanisms. The implications for critical research in educational technology are discussed.     

Pedagogical possibilities for unruly bodies

Project Re?Vision uses disability arts to disrupt stereotypical understandings of disability and difference that create barriers to healthcare. In this paper, we examine how digital stories produced through Re?Vision disrupt biopedagogies by working as body-becoming pedagogies to create non-didactic possibilities for living in/with difference. We engage in meaning making about eight stories made by women and trans people living with disabilities and differences, with our interpretations guided by the following considerations: what these stories ‘teach’ about new ways of living with disability; how these stories resist neoliberalism through their production of new possibilities for living; how digital stories wrestle with representing disability in a culture in which disabled bodies are on display or hidden away; how vulnerability and receptivity become ‘conditions of possibility’ for the embodiments represented in digital stories; and how curatorial practice allows disability-identified artists to explore possibilities of ‘looking back’ at ableist gazes.     

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

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.