Theory-based metrological traceability in education: A reading measurement network

Huge resources are invested in metrology and standards in the natural sciences, engineering, and across a wide range of commercial technologies. Significant positive returns of human, social, environmental, and economic value on these investments have been sustained for decades. Proven methods for calibrating test and survey instruments in linear units are readily available, as are data- and theory-based methods for equating those instruments to a shared unit. Using these methods, metrological traceability is obtained in a variety of commercially available elementary and secondary English and Spanish language reading education programs in the U.S., Canada, Mexico, and Australia. Given established historical patterns, widespread routine reproduction of predicted text-based and instructional effects expressed in a common language and shared frame of reference may lead to significant developments in theory and practice. Opportunities for systematic implementations of teacher-driven lean thinking and continuous quality improvement methods may be of particular interest and value.     

Identification and characterization of information-networks in long-tail data collections

Scientists' ability to synthesize and reuse long-tail scientific data lags far behind their ability to collect and produce these data. Many Earth Science Cyberinfrastructures enable sharing and publishing their data over the web using metadata standards. While profiling data attributes advances the Linked Data approach, it has become clear that building information-networks among distributed data silos is essential to increase their integration and reusability. In this research, we developed a Long-Tail Information-Network (LTIN) model, which uses a metadata-driven approach to build semantic information-networks among datasets published over the web and aggregate them around environmental events. The model identifies and characterizes the spatial and temporal contextual association links and dependencies among datasets. This paper presents the design and application of the LTIN model, and an evaluation of its performance. The model capabilities were demonstrated by inferring the information-network of a stream discharge located at the downstream end of the Illinois River.     

Student perception and usage of an automated programming assessment tool

Automated assessment systems are gaining popularity within computer programming courses. In this paper we perform an empirical evaluation of Mooshak, an online judge that verifies program correctness, in order to determine its usefulness in classroom settings. In particular, we provide a detailed study on how students use the tool, analyze their opinions and critiques about it, and measure other features like its capability to reduce dropout rates. The experience was carried out within a course on algorithm design and analysis where we collected information through several questionnaires and data generated by the tool during the course. Among the main findings we highlight: (1) the usage of the tool was adequate in relation to students’ own testing; (2) its feedback needs to be richer in order to improve its acceptance among students; and (3) there was no statistical evidence to claim Mooshak reduced the dropout rate.     

Ecological patterns of fish distribution in the Slave River Delta region,Northwest Territories,Canada, as relayed by traditional knowledge and Western science

Indigenous community members along the Slave River in Canada have voiced their concerns for the health of ecosystems under pressure from resource extraction, hydroelectric development and global climate change. We present a test case of traditional knowledge and scientific results about the spawning and migration patterns of fish in the Slave River and Delta. This dual knowledge system approach elucidates the broader connectivity of local study regions and can improve monitoring programmes by extending beyond the usual context/confines of the present or recent past, increasing the spatial and temporal range of system information.     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

A web-based,interactive visualization tool for social environmental survey data

Understanding human motivations and actions related to environmental problems is central to modeling complex, human-natural systems. However, social science survey data on environmental issues are often presented in relatively static reports and figures and are not easily accessible for participatory deliberation. Federal data sharing mandates motivate innovative data visualization and sharing mechanisms. We developed an open-source, web-based Survey Data Viewer as a visual interface to explore quantitative social science survey data. We used the Python Django web framework and the D3.js visualization library to create and deploy the tool. The Viewer was implemented using a water-related survey administered to a large, random sample of Utah adults in public venues. The Viewer allows users to visualize question responses based on demographic variables with percentages and mean response levels. We developed a standardized template for encoding survey data and metadata that permits the generalization of the tool to other similar surveys.     

大学科技英语写作教学现状调查与分析

写作可以促进全面的语言技能的发展,这是国内外许多专家学者达成的共识.本文通过对沈阳工学院450名一年级本科生的调查和分析得出了许多有益的数据.文章在调查分析的基础上,对大学科技英语写作教学提出了一些改进建议.     

科技时代下药物分析课程改革

随着科学技术高速发展,国内医药的研制高速发展,这就使得与其紧密相关的要写专业中药物分析课程的学习难度不断加大,学校为了培养学生满足新时代的需求就必须主动迎接这一新的挑战.为了适应新时代培养高素质药学专业人才的需要,药物分析课程必须适时进行改革,本文根据教学中的经验,对药物分析课程改革提出了一些看法和设想.     

2010 award winners.

This issue announces the 2010 award winners from the American Psychological Association. In accordance with established custom, the APA bestows annual awards in recognition of outstanding contributions by psychologists across an array of domains. The awards laud distinguished contributions to the advancement of science, to applications of psychological knowledge to clinical and social issues, to education and training, and to work devoted to international cooperation and humanitarian ideals. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

实施科技制作与提高机械专业大学生综合能力的研究与实践

简述了当前经济社会发展对机械类专业技术人才的能力要求,分析了科技制作对促进大学生综合能力培养的作用及实施要点,通过所辅导的一个项目实例说明实施科技制作可以极大地提高机械专业大学生综合能力,是对当前教学方法的有益补充,该培养方法对当前研究型和技术应用型本科教育具有较好的参考与践行价值。