What teachers think about what they can do with data: Development and validation of the data driven decision-making efficacy and anxiety inventory

Classroom level data driven decision-making (DDDM) involves the use of data to identify patterns of performance that reveal students’ academic strengths and weaknesses relative to established learning goals, and the planning of instructional practices to support academic success for all students. Although DDDM is not a new paradigm in education, little is known about what variables facilitate teacher adoption of DDDM practices. The aim of this work was to introduce two such variables, DDDM efficacy and DDDM anxiety, and a measure of these constructs, the DDDM efficacy and anxiety (3D-MEA) inventory. The 1728 participants in this study were K-12 teachers who had experienced varying levels of DDDM professional development in a Pacific Northwestern state. Exploratory factor analysis (EFA) (n = 864) and confirmatory factor analysis (CFA) (n = 864) were utilized to evaluate the psychometric properties of the 3D-MEA Inventory. Results supported a five-factor model. A discussion of the results and their implications ensue.     

Snakes and ladders: stressing the role of meta-competencies for post-modern careers

Major global changes during the last decades have influenced the individual’s work-life and career. As a result of globalisation, increasing societal complexity and flexibility, careers have lost their linearity and predictability. Traditional models of career development no longer provide a comprehensive explanation for an adequate career development. New theoretical concepts such as protean and boundaryless career attitudes are presented as options to cope with the new situation. In addition, the development of career meta-competencies and skills are introduced as an approach to foster career self-management.     

Physiological and anthropometric characteristics of top-level youth cross-country cyclists

In the literature there is a lack of data about the development of top level athletes in cross-country mountain biking (XCO). The purpose of this study was to analyze anthropometric and physiological characteristics of some of the best XCO bikers aged between 13 and 16. The study involved 45 bikers (26 males and 19 females) belonging to a youth national team. The evaluations, consisting of anthropometric measures, incremental cycling tests (VO_2max, PPO, P@RCP), and 30 s Wingate Tests (PMax, PMean), were conducted over a lapse of 4 years. Our findings showed in bikers, already at young age, a specific athletic profile advantageous for XCO performance. At the age of 16, just before entering the junior category and competing at international level, male and female bikers showed physiological values normalized to the body mass comparable to those reported in literature for high level athletes (VO_2max>70 and >60 ml/kg/min, PPO >6.5 and >5.5 W/kg, respectively in males and females). The production of high power-to-weight ratios and high peaks of anaerobic power attests the presence of highly developed aerobic and anaerobic systems in young XCO cyclists reflecting the high physiological demand of this sport.     

Preferential text classification: learning algorithms and evaluation measures

In many applicative contexts in which textual documents are labelled with thematic categories, a distinction is made between the primary categories of a document, which represent the topics that are central to it, and its secondary categories, which represent topics that the document only touches upon. We contend that this distinction, so far neglected in text categorization research, is important and deserves to be explicitly tackled. The contribution of this paper is threefold. First, we propose an evaluation measure for this preferential text categorization task, whereby different kinds of misclassifications involving either primary or secondary categories have a different impact on effectiveness. Second, we establish several baseline results for this task on a well-known benchmark for patent classification in which the distinction between primary and secondary categories is present; these results are obtained by reformulating the preferential text categorization task in terms of well established classification problems, such as single and/or multi-label multiclass classification; state-of-the-art learning technology such as SVMs and kernel-based methods are used. Third, we improve on these results by using a recently proposed class of algorithms explicitly devised for learning from training data expressed in preferential form, i.e., in the form “for document d _i , category c′ is preferred to category c′′”; this allows us to distinguish between primary and secondary categories not only in the classification phase but also in the learning phase, thus differentiating their impact on the classifiers to be generated.     

Stability of Markov jump systems with quadratic terms and its application to RLC circuits

The paper presents results for the second moment stability of continuous-time Markov jump systems with quadratic terms, aiming for engineering applications. Quadratic terms stem from physical constraints in applications, as in electronic circuits based on resistor (R), inductor (L), and capacitor (C). In the paper, an RLC circuit supplied a load driven by jumps produced by a Markov chain—the RLC circuit used sensors that measured the quadratic of electrical currents and voltages. Our result was then used to design a stabilizing controller for the RLC circuit with measurements based on that quadratic terms. The experimental data confirm the usefulness of our approach.     

Long pentraxin 3 as a marker of COVID-19 severity: evidences and perspectives

IntroductionSeveral laboratory tests are characteristically altered in Coronavirus Disease 2019 (COVID-19), but are not totally accurate in predicting the disease outcome. The long pentraxin 3 (PTX3) is quickly released directly at inflammation sites by many immune cell types. Previous studies have shown that PTX3 correlated with disease severity in various inflammatory conditions. Our study investigated the use of PTX3 as a potential marker of COVID-19 severity and compared its performance in detecting a more severe form of the disease with that of routine laboratory parameters.Materials and methodsStored serum samples of RT-PCR confirmed COVID-19 cases that had been obtained at hospital admission were retrospectively analysed. Intensive care unit (ICU) stay was considered a surrogate endpoint of severe COVID-19. Pentraxin 3 was measured by a commercial enzyme-linked immunosorbent assay.ResultsA total of 96 patients were recruited from May 1st, 2020 to June 30th, 2020; 75/96 were transferred to ICU. Pentraxin 3 was higher in ICU vs non-ICU patients (35.86 vs 10.61 ng/mL, P < 0.001). Univariate and multivariate logistic regression models demonstrated that the only significant laboratory predictor of ICU stay was PTX3 (OR: 1.68 (1.19-2.29), P = 0.003), after controlling for comorbidities. The Receiver Operator Characteristic curve analysis showed that PTX3 had a higher accuracy compared to C-reactive protein (CRP), lactate dehydrogenase (LD), ferritin in identifying ICU patients (AUC of PTX3 = 0.98; CRP = 0.66; LD = 0.70; ferritin = 0.67, P < 0.001). A cut-off of PTX3 > 18 ng/mL yielded a sensitivity of 96% and a specificity of 100% in identifying patients requiring ICU.ConclusionHigh values of PTX3 predict a more severe COVID-19.     

Taiwanese families' perspectives on learning disabilities: an exploratory study in three middle schools

The purpose of this study was to investigate families' perspectives on learning disabilities (LD) in Taiwan. A researcher‐designed survey was sent out to families of children with or without LD from three middle schools. Data from 153 participants were gathered and analysed. The survey consisted of four sections, namely demographic information, children's reading performance, families' general understanding and perspectives of LD. Specifically, the last section of 54 Likert scale items designed to gather information about families' perspectives of LD was analysed with five domains: causes of LD (inherent or acquired), conceptions and misconceptions, attitudes (acceptance or rejection), disclosure of disabilities (willing or unwilling), and support. Results indicated that families were not familiar with educational services related to LD in schools. Additionally, families' experiences with people with disabilities resulted in different perspectives regarding the causes and conceptions about LD, and acquiring support. Families' perspectives on LD also correlated with personal characteristics, including the level of their children's reading performance, number of children and income level. The cultural implications of these findings interplay with an emphasis on academic performance are discussed.     

幼儿足球舞练习对幼儿生长发育影响初探

本文通过实验对比,证实了足球舞练习不仅可以促进幼儿生长发育,还可提高幼儿的身体素质,丰富幼儿的体育锻炼内容。