Crossing MGLS with the Middle Grades Research Agenda: A Guide for Researchers

For the past several years, leaders in middle grades education research have strengthened their call for more methodologically robust quantitative research to address important questions in the field. Recently, two important routes towards addressing this call have emerged: the Middle Grades Longitudinal Study from the National Center for Education Statistics, and a new research agenda from the Middle Level Education Research Special Interest Group of the American Educational Research Association. In this paper, we conduct a content analysis of the items in the forthcoming longitudinal study in light of the extant research agenda. Results indicate that research questions in eight sections of the agenda are moderately to well-addressed by the data, and that the longitudinal study will provide rich contextual data related to many others. The concurrent emergence of the research agenda and this data offers an opportunity for the research community to engage in high-level quantitative research with a middle grades lens to inform future policy. The item-by-item crosswalk available for download (scroll down for link below) provides guidance for researchers using the Middle Grades Longitudinal Study data to address questions from the research agenda

Active templates: Manipulating pointers with pictures

Active templates are a semi-automatic visual mechanism for generating algorithms for manipulating pointer-based data structures. The programmer creates a picture showing the affected part of a data structure before and after a general-case manipulation. Code for the operation is compiled directly from the picture, which also provides the development environment with enough information to generate, automatically, a series of templates for other similar pictures, each describing a different configuration which the data structure may possess. The programmer completes the algorithm by creating matching after-pictures for each of these cases. At every stage, most of the picture-generation is automatic. Much of the tedious detail of conventional pointer-based data-structure manipulation, such as maintenance of current pointers, is unnecessary in a system based on active templates

Creative or Not? Birds and Ants Draw with Muscle

In this work, a novel approach of merging two swarm intelligence algorithms is considered – one mimicking the behaviour of ants foraging (Stochastic Diffusion Search [5]) and the other algorithm simulating the behaviour of birds flocking (Particle Swarm Optimisation [17]). This hybrid algorithm is assisted by a mechanism inspired from the behaviour of skeletal muscles activated by motor neurons. The operation of the swarm intelligence algorithms is first introduced via metaphor before the new hybrid algorithm is defined. Next, the novel behaviour of the hybrid algorithm is reflected through a cooperative attempt to make a drawing, followed by a discussion about creativity in general and the ’computational creativity’ of the swarm

Cooperation of Nature and Physiologically Inspired Mechanism in Visualisation

A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants – Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells in blood vessels, where the concept of high and low blood pressure is utilised. The performance of the nature-inspired algorithms and the biologically inspired mechanisms in the hybrid algorithm is reflected through a cooperative attempt to make a drawing on the canvas. The scientific value of the marriage between the two swarm intelligence algorithms is currently being investigated thoroughly on many benchmarks and the results reported suggest a promising prospect (al-Rifaie, Bishop & Blackwell, 2011). We also discuss whether or not the ‘art works’ generated by nature and biologically inspired algorithms can possibly be considered as ‘computationally creative’