A taxonomic view of pretend play activity among 4- and 5-year-old children

Based on findings from a 6-month study that explored the nature of literate behavior in the pretend play episodes of eight preschoolers, a taxonomy of their pretend play activity is described and analyzed. Relationships between the different kinds of pretend play activity as demonstrated by these youngsters are examined in light of the taxonomy and other research. Implications of certain kinds of pretend play activity for emergent literacy development are also briefly discussed.     

Three-dimensional printing-based electro-millifluidic devices for fabricating multi-compartment particles

In this work, we demonstrate the use of stereolithographic 3D printing to fabricate millifluidic devices, which are used to engineer particles with multiple compartments. As the 3D design is directly transferred to the actual prototype, this method accommodates 3D millimeter-scaled features that are difficult to achieve by either lithographic-based microfabrication or traditional macrofabrication techniques. We exploit this approach to produce millifluidic networks to deliver multiple fluidic components. By taking advantage of the laminar flow, the fluidic components can form liquid jets with distinct patterns, and each pattern has clear boundaries between the liquid phases. Afterwards, droplets with controlled size are fabricated by spraying the liquid jet in an electric field, and subsequently converted to particles after a solidification step. As a demonstration, we fabricate calcium alginate particles with structures of (1) slice-by-slice multiple lamellae, (2) concentric core-shells, and (3) petals surrounding the particle centers. Furthermore, distinct hybrid particles combining two or more of the above structures are also obtained. These compartmentalized particles impart spatially dependent functionalities and properties. To show their applicability, various ingredients, including fruit juices, drugs, and magnetic nanoparticles are encapsulated in the different compartments as proof-of-concepts for applications, including food, drug delivery, and bioassays. Our 3D printed electro-millifluidic approach represents a convenient and robust method to extend the range of structures of functional particles.     

What mothers do: everyday routines and rituals and their impact upon young children’s use of drawing for meaning making

This paper draws upon evidence from a three‐year longitudinal study of young children drawing across home, pre‐school and school. The study shows how the belief systems of significant adults and more able peers/siblings impact upon the child’s access to, use of and beliefs about drawing. Concentrating upon the children when in the Foundation Stage (aged between three and five), the paper highlights:

• the importance of the mother’s role in organizing the home space, the child’s time and his or her access to materials;

• gendered responses to an environment in which the mother is a constant presence in comparison with the limited presence of the father.

     

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.