An online community designed to support future makers in educational reform

Australian education is undergoing national reform at many levels. The school sector, where preservice teachers will be employed, are adjusting to the demands of the National Curriculum and improving teacher quality through the National Professional Standards for Teachers. In addition, the university sector, where pre-service teachers are prepared, is undergoing its own education reform through the introduction of a demand-driven system and ensuring quality for tertiary education interns through the Higher Education Standards Framework. In moving to prepare preservice teachers for the school system; universities are grappling with the double-barreled approach to teacher quality; quality within the university course and quality within the student teachers being prepared. Through a collaborative partnership including university lecturers, Department of Education central administration staff, school principals, school coordinators, practicum supervisors, mentor teachers and pre-service teachers; the stakeholders have formed an online community of learners engaging in reflective practice who are committed to improving teacher quality. This online community not only links the key stakeholders within the project, it facilitates the nexus between theory and practice often missing in our pre-service teacher placements. This paper reports preliminary data about an initiative to ensure final year pre-service teachers are aspiring to meet the graduate professional standards through the use of an innovative online community.

Knowledge management research in the construction industry: a review

Purpose: Knowledge management (KM) is important to the knowledge-intensive construction industry. The diversified and changing nature of works in this field warrants us to stocktake, identify changes and map out KM research framework for future exploration. Design/methodology/approach: The study involves three aspects. First, three stages of KM research in construction were distinguished in terms of the time distribution of 217 target publications. Major topics in the stages were extracted for understanding the changes of research emphasis from evolutionary perspective. Second, the past works were summed up in a three-dimensional research framework in terms of management organization, managerial methodology and approach, and managerial objective. Finally, potential research orientations in the future were predicted to expand the existing research framework. Findings: It was found that (1) KM research has significantly blossomed in the last two decades with a great potential; (2) major topics of KM were changing in terms of technology, technique, organization, attribute of knowledge and research objectives; (3) past KM studies centred around management organization, managerial methodology and approach, and managerial objective thus a three-dimensional research framework was proposed; (4) within the research framework, team-level, project-level and firm-level KM were studied to achieve project, organizational and competitive objectives by integrated methodologies of information technology, social technique and KM process tool; and (5) nine potential research orientations were predicted corresponding to the three dimensions. Finally, an expanded research framework was proposed to encourage and guide future research works in this field. Research limitations/implications: The paper only focused on the construction industry. The findings need further exploration in order to discover any possible missing important research works which were not published in English or not included in the time period. Originality/value: The paper formed a systematic framework of KM research in construction and predicted the potential research orientations. It provides much value for the researchers who want to understand the past and the future of global KM research in the construction industry.

Electronic coupling and catalytic effect on H2 evolution of MoS2/graphene nanocatalyst

Inorganic nano-graphene hybrid materials that are strongly coupled via chemical bonding usually present superior electrochemical performance. However, how the chemical bond forms and the synergistic catalytic mechanism remain fundamental questions. In this study, the chemical bonding of the MoS2 nanolayer supported on vacancy mediated graphene and the hydrogen evolution reaction of this nanocatalyst system were investigated. An obvious reduction of the metallic state of the MoS2 nanolayer is noticed as electrons are transferred to form a strong contact with the reduced graphene support. The missing metallic state associated with the unsaturated atoms at the peripheral sites in turn modifies the hydrogen evolution activity. The easiest evolution path is from the Mo edge sites, with the presence of the graphene resulting in a decrease in the energy barrier from 0.17 to 0.11 eV. Evolution of H2 from the S edge becomes more difficult due to an increase in the energy barrier from 0.43 to 0.84 eV. The clarification of the chemical bonding and catalytic mechanisms for hydrogen evolution using this strongly coupled MoS2/graphene nanocatalyst provide a valuable source of reference and motivation for further investigation for improved hydrogen evolution using chemically active nanocoupled systems.

Statistical tools for analyzing water quality data

Water quality data are often collected at different sites over time to improve water quality management. Water quality data usually exhibit the following characteristics: non-normal distribution, presence of outliers, missing values, values below detection limits (censored), and serial dependence. It is essential to apply appropriate statistical methodology when analyzing water quality data to draw valid conclusions and hence provide useful advice in water management. In this chapter, we will provide and demonstrate various statistical tools for analyzing such water quality data, and will also introduce how to use a statistical software R to analyze water quality data by various statistical methods. A dataset collected from the Susquehanna River Basin will be used to demonstrate various statistical methods provided in this chapter. The dataset can be downloaded from website http://www.srbc.net/programs/CBP/nutrientprogram.htm.