The potential contributions of concept maps for learning website to assessment for learning practices

The purpose of this paper is to examine the promising contributions of the Concept Maps for Learning (CMfL) website to assessment for learning practices. The CMfL website generates concept maps from relatedness degree of concepts pairs through the Pathfinder Scaling Algorithm. This website also confirms the established principles of effective assessment for learning, for it is capable of automatically assessing students' higher order knowledge, simultaneously identifying strengths and weaknesses, immediately providing useful feedback and being user-friendly. According to the default assessment plan, students first create concept maps on a particular subject and then they are given individualized visual feedback followed by associated instructional material (e.g., videos, website links, examples, problems, etc.) based on a comparison of their concept map and a subject matter expert's map. After studying the feedback and instructional material, teachers can monitor their students' progress by having them create revised concept maps. Therefore, we claim that the CMfL website may reduce the workload of teachers as well as provide immediate and delayed feedback on the weaknesses of students in different forms such as graphical and multimedia. For the following study, we will examine whether these promising contributions to assessment for learning are valid in a variety of subjects.

Exploring the mobile structural assessment tool: Concept maps for learning website: Concept Maps for Learning Website

In this paper, we describe how the pathfinder algorithm converts relatedness ratings of concept pairs to concept maps; we also present how this algorithm has been used to develop the Concept Maps for Learning website (www.conceptmapsforlearning.com) based on the principles of effective formative assessment. The pathfinder networks, one of the network representation tools, claim to help more students memorize and recall the relations between concepts than spatial representation tools (such as Multi- Dimensional Scaling). Therefore, the pathfinder networks have been used in various studies on knowledge structures, including identifying students’ misconceptions. To accomplish this, each student’s knowledge map and the expert knowledge map are compared via the pathfinder software, and the differences between these maps are highlighted. After misconceptions are identified, the pathfinder software fails to provide any feedback on these misconceptions. To overcome this weakness, we have been developing a mobile-based concept mapping tool providing visual, textual and remedial feedback (ex. videos, website links and applets) on the concept relations. This information is then placed on the expert concept map, but not on the student’s concept map. Additionally, students are asked to note what they understand from given feedback, and given the opportunity to revise their knowledge maps after receiving various types of feedback.

Efficient and Selectable Production of Reactive Species Using a Nanosecond Pulsed Discharge in Gas Bubbles in Liquid

A plasma gas bubble-in-liquid method for high production of selectable reactive species using a nanosecond pulse generator has been developed. The gas of choice is fed through a hollow needle in a point-to-plate bubble discharge, enabling improved selection of reactive species. The increased interface reactions, between the gas-plasma and water through bubbles, give higher productivity. H₂O₂ was the predominant species produced using Ar plasma, while predominantly  and NO₂ were generated using air plasma, in good agreement with the observed emission spectra. This method has nearly 100% selectivity for H₂O₂, with seven times higher production, and 92% selectivity for , with nearly twice the production, compared with a plasma above the water.

Eighty years 'Owre the Sea': Robert Burns and the early United States of America, c. 1786-1866.

This thesis represents the first extensive critical study of the relationship between Robert Burns and the early United States of America. Spanning literature, history and memory studies, the following chapters take an interdisciplinary approach towards investigating the methods by which Burns and his works rose to prominence and came to be of cultural and literary significance in America. Theoretically, these converging disciplines intersect through a transnational, Atlantic Studies perspective that shifts emphasis from Burns as the 'national poet of Scotland' onto the various socio-cultural connections that facilitated the spread of his work and reputation. In addition to Scottish literary studies, the thesis contributes to the broader fields of Transatlantic, Transnational and American Studies. Previous studies have suggested that Burns's popularity in the early United States might be attributed to his kinship with 'national' American ideals of freedom, egalitarianism and individual liberty. While some of the evidence supports this claim, this thesis argues that it also wrongly assumes a spatiotemporal unity for the nineteenth-century American nation. It concludes by suggesting that future critical studies of the poet must heed the multifarious complexities of 'national' paradigms, pointing the way to further work on the reception and influence of Burns in other 'global' or, indeed, transnational contexts.

Real-time alert correlation with type graphs

The premise of automated alert correlation is to accept that false alerts from a low level intrusion detection system are inevitable and use attack models to explain the output in an understandable way. Several algorithms exist for this purpose which use attack graphs to model the ways in which attacks can be combined. These algorithms can be classified in to two broad categories namely scenario-graph approaches, which create an attack model starting from a vulnerability assessment and type-graph approaches which rely on an abstract model of the relations between attack types. Some research in to improving the efficiency of type-graph correlation has been carried out but this research has ignored the hypothesizing of missing alerts. Our work is to present a novel type-graph algorithm which unifies correlation and hypothesizing in to a single operation. Our experimental results indicate that the approach is extremely efficient in the face of intensive alerts and produces compact output graphs comparable to other techniques.