A Case Study on Targeted Support Using TPACK Model for Newly Hired Secondary Mathematics Teachers

As teachers plan for instruction, technology integration is an important factor in the planning and implementation process. This is become imperative in a virtual learning environment for instructors to be competent (Gregory & Lodge, 2015). Problems exist with integrating technology that aligns with teaching and learning in content areas. Among the many possible factors that contribute to these problems is lack of understanding of technology, lack of support for teachers with technology, everchanging technology tools, inadequate training alignment to instruction, technology training that is not content-specific, lack of support with the integration of technology, pedagogy, and content (Koehler et al., 2013). This case study with an intervention focused on investigating the essential characteristics of planning and implementing lessons with newly hired secondary mathematics teachers. A mixed methods design was employed to provide triangulation of multiple data points to validate key findings. The TPACK (Technological, Pedagogical, and Content Knowledge) framework by Mishra and Koehler (2006) provided a guide for planning and implementing lessons as well as to build teachers\u27 confidence in the integration of technology during instruction. Through planning interviews, survey data, class observations, teacher reflections, field notes, and teacher artifacts of lessons, the researcher examined the essential characteristics of planning and implementing a lesson using the TPACK model. Findings indicated that use of the TPACK model provided support for newly hired mathematics teachers in their incorporation of technology into instruction. Eight implications emerged from the findings in the study: using the TPACK survey to customize training for teachers by identifying areas of support, using the TPACK model for virtual planning, contextual knowledge in virtual classrooms, comprehending technology, implications of software-focused and use of sample lessons, virtual professional development with TPACK model, level of support with TPACK Planning, and TPACK survey interviews. This research informs practitioners and researchers to understand the complexity of teaching and the importance of providing differentiated support and training based on the needs of new teachers

"Cielo en la tierra"

En un siglo en el que hay un quiebre y declive entre la sociedad y la religión, se hace necesario una cultura de tolerancia hacia la fe de cada individuo. Por ello, el presente estudio está encaminado a dicho tema. La investigación tiene como propósito conocer la percepción de los jóvenes y adultos ante el tema de la fe. Asimismo, el trabajo ha sido realizado a raíz de la exposición fotográfica “Cielo en la Tierra”, en la cual se realizó un sondeo de opiniones a diferentes participantes. La exposición fotográfica tiene como objetivos representar las demostraciones de devoción, alabanza y adoración expresadas por las personas y reconstruir gráficamente lo que los creyentes entienden por encuentro íntimo y relación con la divinidad mediante las imágenes. En el sondeo es donde se desarrolla la presente investigación, ya que se analizan patrones de conductas y sistemas de valores que se dieron en la exposición. Al igual que un análisis para descubrir la percepción hacia la fe mediante lo expuesto en las fotografías

The role of personality traits and social support in relations of health-related behaviours and depressive symptoms

Background: Previous evidence has suggested that physically inactive individuals and extensive media users are at high risk for experiencing depressive symptoms. We examined personality traits and perceived social support as potential moderators of this association. Personality and perceived social support were included as two of the most frequently considered variables when determining predispositioning factors for media use phenomena also discussed in relation to physical activity. Methods: We analysed cross-sectional data from 1402 adults (18–31 years old) who participated in a national health survey in Germany (KiGGS, Study on the health of children and adolescents in Germany, wave 2). The data included one-week accelerometer assessments as objective indicators of physical activity, self-reported media use, depressive symptoms, perceived social support and Big 5 personality traits. An elastic net regression model was fit with depressive symptoms as outcome. Ten-fold cross-validation was implemented. Results: Amongst the main effects, we found that high media use was positively correlated with depressive symptoms, whereas physical activity was not correlated. Looking at support and individual differences as moderators, revealed that PC use was more strongly correlated with depressive symptoms in cases of low levels of perceived social support. Positive associations of social media use with depressive symptoms were more pronounced, whereas negative associations of moderate to vigorous physical activity with depressive symptoms were less pronounced in extraverts than they were in introverts. Conclusions: Results highlight the importance of considering individual factors for deriving more valid recommendations on protective health behaviours.Peer Reviewe

Small area estimation in R with application to Mexican income data

In the last decades policy decisions are often based on statistical measures. The more detailed this information is, the better is the basis for targeting policies and evaluating policy programs. For instance, the United Nations suggest more disaggregation of statistical indicators for monitoring their Sustainable Development Goals and also the number of National Statistical Institutes (NSIs) that notice the need of more disaggregated statistics is increasing. Dimensions for disaggregation can be characteristics of the individuals or households like sex, age or ethnicity, economic activity or spatial dimensions like metropolitan areas or districts. Primary data sources for variables that are used to estimate statistical indicators are national household surveys. However, sample sizes are usually small or even zero at disaggregated levels. Therefore, direct estimators based only on survey data can be unreliable or not available for small domains. While the option of more specific surveys is costly, model-based methodologies for dealing with small sample sizes can help to obtain reliable estimates for small domains. The so-called Small Area Estimation (SAE) methods [1,2] link survey data that is only available for a proportion of households with administrative or census data available for all households in the area of interest. Even though a wide range of SAE methods is proposed by academic researchers, these are, so far, applied only by a small number of NSIs or other practitioners like the World Bank. This gap between theoretical possibilities and practical application can have several reasons. One reason can be the lack of suitable statistical software. The free software environment R helps to counteract this issue since researchers can make their codes available to the public via packages. Thus, new methods can reach the practitioner faster than with non-free software. The next two sections summarize which packages are already available and what could be improved in the future

The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators

The R package emdi offers a methodological and computational framework for the estimation of regionally disaggregated indicators using small area estimation methods and provides tools for assessing, processing and presenting the results. A range of indicators that includes the mean of the target variable, the quantiles of its distribution and complex, non-linear indicators or customized indicators can be estimated simultaneously using direct estimation and the empirical best predictor (EBP) approach (Molina and Rao 2010). In the application presented in this paper package emdi is used for estimating inequality indicators and the median of the income distributions for small areas in Austria. Because the EBP approach relies on the normality of the mixed model error terms, the user is further assisted by an automatic selection of data-driven transformation parameters. Estimating the uncertainty of small area estimates (using a mean squared error - MSE measure) is achieved by using both parametric bootstrap and semi-parametric wild bootstrap. The additional uncertainty due to the estimation of the transformation parameter is also captured in MSE estimation. The semi-parametric wild bootstrap further protects the user against departures from the assumptions of the mixed model in particular, those of the unit-level error term. The bootstrap schemes are facilitated by computationally effcient code that uses parallel computing. The package supports the users beyond the production of small area estimates. Firstly, tools are provided for exploring the structure of the data and for diagnostic analysis of the model assumptions. Secondly, tools that allow the spatial mapping of the estimates enable the user to create high quality visualizations. Thirdly, results and model summaries can be exported to Excel™ spreadsheets for further reporting purposes

The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators

The R package emdi enables the estimation of regionally disaggregated indicators using small area estimation methods and includes tools for processing, assessing, and presenting the results. The mean of the target variable, the quantiles of its distribution, the headcount ratio, the poverty gap, the Gini coefficient, the quintile share ratio, and customized indicators are estimated using direct and model-based estimation with the empirical best predictor (Molina and Rao 2010). The user is assisted by automatic estimation of datadriven transformation parameters. Parametric and semi-parametric, wild bootstrap for mean squared error estimation are implemented with the latter offering protection against possible misspecification of the error distribution. Tools for (a) customized parallel computing, (b) model diagnostic analyses, (c) creating high quality maps and (d) exporting the results to Excel and OpenDocument Spreadsheets are included. The functionality of the package is illustrated with example data sets for estimating the Gini coefficient and median income for districts in Austria

Development of a gp60-subtyping method for Cryptosporidium felis

Background: Feline cryptosporidiosis is an increasing problem, especially in catteries. In humans, close contact with cats could be a potential source of infection although the risk of contracting cryptosporidiosis caused by Cryptosporidium felis is considered to be relatively low. Sequencing of the 60-kDa glycoprotein gene is a commonly used tool for investigation of the genetic diversity and transmission dynamics of Cryptosporidium species. However, until now the sequence of gp60 from C. felis has not been available and genotyping has been limited to less discriminatory markers, such as 18S rRNA, COWP and HSP70. Methods: We have identified the gp60 orthologue within the genome sequence of C. felis, and used the sequence to design a nested PCR for subtyping purposes. A total of 128 clinical isolates of both feline and human origin, were used to evaluate the marker. Results: Sequence analysis revealed large variations between the different samples. The C. felis gp60 lack the characteristic serine-tract found in many other cryptosporidian orthologues, instead it has an insertion of variable length (361-742 nt). Also, two cases of suspected zoonotic transmission of C. felis between cats and humans were successfully confirmed. Conclusions: We have identified the gp60 gene in C. felis and show how this highly variable marker can be used in epidemiological investigations

Description and Pilot Evaluation of a Dreamer Ally Training for Higher Education Staff and Faculty

21 pagesWe describe a Dreamer Ally training provided to staff and faculty on a university campus and present results of a pilot evaluation of this training. The Dreamer Ally training was designed to (a) increase university faculty and staff awareness, understanding, and self-efficacy for working with Dreamer students and (b) stimulate action to make the campus more responsive to the challenges and contributions of Dreamer students. For the purpose of this study we define Dreamer students as inclusive of undocumented students, students with the temporary protection of DACA (Deferred Action for Childhood Arrivals), students who qualify for the state’s tuition equity program, and students from mixed legal status families. Study goals were to describe the training, gather pilot data on participant learning goals, post-training satisfaction and self-efficacy for supporting Dreamer students, and generate participant feedback about utility of training components and their plans for subsequent action. Participants completed questionnaires before and after the training. Responses to open-ended questions indicated that most participants attended in order to learn how to better support Dreamer students. Paired samples (pre and post) t-tests indicated significantly higher self-efficacy for supporting Dreamer students at posttest. Participant satisfaction with the training was high and found the information session content and working through different Dreamer student scenarios most useful. Action plans included changing program or unit websites to be more inclusive of Dreamers. Limitations include the absence of a control group. Findings can inform institutional efforts to raise faculty and staff awareness of and responsiveness to the challenges facing Dreamer students

Human CLEC9A antibodies deliver Wilms' tumor 1 (WT1) antigen to CD141+ dendritic cells to activate naïve and memory WT1‐specific CD8+ T cells

Objectives Vaccines that prime Wilms' tumor 1 (WT1)‐specific CD8+ T cells are attractive cancer immunotherapies. However, immunogenicity and clinical response rates may be enhanced by delivering WT1 to CD141+ dendritic cells (DCs). The C‐type lectin‐like receptor CLEC9A is expressed exclusively by CD141+ DCs and regulates CD8+ T‐cell responses. We developed a new vaccine comprising a human anti‐CLEC9A antibody fused to WT1 and investigated its capacity to target human CD141+ DCs and activate naïve and memory WT1‐specific CD8+ T cells. Methods WT1 was genetically fused to antibodies specific for human CLEC9A, DEC‐205 or β‐galactosidase (untargeted control). Activation of WT1‐specific CD8+ T‐cell lines following cross‐presentation by CD141+ DCs was quantified by IFNγ ELISPOT. Humanised mice reconstituted with human immune cell subsets, including a repertoire of naïve WT1‐specific CD8+ T cells, were used to investigate naïve WT1‐specific CD8+ T‐cell priming. Results The CLEC9A‐WT1 vaccine promoted cross‐presentation of WT1 epitopes to CD8+ T cells and mediated priming of naïve CD8+ T cells more effectively than the DEC‐205‐WT1 and untargeted control‐WT1 vaccines. Conclusions Delivery of WT1 to CD141+ DCs via CLEC9A stimulates CD8+ T cells more potently than either untargeted delivery or widespread delivery to all Ag‐presenting cells via DEC‐205, suggesting that cross‐presentation by CD141+ DCs is sufficient for effective CD8+ T‐cell priming in humans. The CLEC9A‐WT1 vaccine is a promising candidate immunotherapy for malignancies that express WT1