Education Commons^™

Pirls 2016 Australian Year 4 Data [Sas], Sue Thomson, Kylie Hillman, Marina Schmid, Sima Rodrigues, Jessica Fullarton

Kylie Hillman

This dataset (SAS zipped) is a data source for the report PIRLS 2016: Reporting Australia's results. Refer to the readme.txt file for details.

Pirls 2016 Australian Year 4 Data [Sas], Sue Thomson, Kylie Hillman, Marina Schmid, Sima Rodrigues, Jessica Fullarton

Dr Sue Thomson

This dataset (SAS zipped) is a data source for the report PIRLS 2016: Reporting Australia's results. Refer to the readme.txt file for details.

Pirls 2016 Australian Year 4 Data [Spss], Sue Thomson, Kylie Hillman, Marina Schmid, Sima Rodrigues, Jessica Fullarton

Dr Sue Thomson

This dataset (SPSS zipped) is a data source for the report PIRLS 2016: Reporting Australia's results. Refer to the readme.txt file for details.

Australian Child Wellbeing Project Technical Report, Petra Lietz, Elizabeth O'Grady, Mollie Tobin, Martin Murphy, Greg Macaskill, Gerry Redmond, Katherine Dix, Sue Thomson

Dr Katherine Dix

The Australian Child Wellbeing Project (ACWP) was conducted from 2012 – 2015 using a mixed-methods, child-centred approach. Young people’s perspectives were used to inform a nationally representative survey of children’s wellbeing in the middle years. The aims of the ACWP survey were to benchmark child wellbeing in Australia and to provide useful information for services that promote young people’s healthy development. A final outcome of this project was to make the extensive ACWP database publically available in order to provide further opportunity to exploit the rich data and improve understanding. Accordingly, this technical report consolidates the research conducted by ACER …

Using Data To Evaluate Performance And Inform Decisions, Lynn D. Akey

Lynn D. Akey, Ph.D.

No abstract provided.

Transcending Institutions And Borders: 21st Century Digital Scholarship At K-State, Rebel Cummings-Sauls, Rachel Miles, Ryan Otto, Charlene N. Simser

Rebel Cummings-Sauls

Digital scholarship of the 21st century transcends institutions and borders with its freedom from print and physical locations. This case study reviews aspects of establishing a sustainable digital scholarship center, supporting open access through the institutional repository (K-State Research Exchange - K-REx) and an open access publishing platform (New Prairie Press – NPP) along with other outreach efforts. The Center for the Advancement of Digital Scholarship (CADS) at K-State Libraries serves our campus community, but digital scholarship extends K-State's impact far beyond Manhattan, Kansas. Highlighting the scholarship at our campus is only one small piece of the landscape. Collaboration on …

Australian Child Wellbeing Project Technical Report, Petra Lietz, Elizabeth O'Grady, Mollie Tobin, Martin Murphy, Greg Macaskill, Gerry Redmond, Katherine Dix, Sue Thomson

Dr Martin Murphy

The Australian Child Wellbeing Project (ACWP) was conducted from 2012 – 2015 using a mixed-methods, child-centred approach. Young people’s perspectives were used to inform a nationally representative survey of children’s wellbeing in the middle years. The aims of the ACWP survey were to benchmark child wellbeing in Australia and to provide useful information for services that promote young people’s healthy development. A final outcome of this project was to make the extensive ACWP database publically available in order to provide further opportunity to exploit the rich data and improve understanding. Accordingly, this technical report consolidates the research conducted by ACER …

Utilizing Data To Write Meaningful Slo’S That Pertain To School Counseling, Thomas Keller, Brandie M. Oliver, Nick R. Abel

Brandie M. Oliver

Learning session conducted at the Guidance & Counseling Workshop, Muncie, IN. 2014, April.

Data Used For Manuscript - The Effect Of Active Learning Spaces And Teaching Philosophy On Professors' Instructional Practices, Nyaradzo Mvududu, Kim Sawers, David A. Wicks

David A Wicks

No abstract provided.

Using Data To Make Decisions About Student Services, Tonisha B. Lane, Larry D. Long

Larry D. Long

First-generation and low-income students are increasingly matriculating into post-secondary education. Acclimating first-generation and low-income students to higher education presents unique challenges and opportunities to improve institutional support and services. This program will inform participants about how Michigan State University used multiple data sources to assess, intervene, and support students from first-generation and low-income backgrounds involved in the Student Success Initiative. This program adds to the emerging conversation on using data to understand students and make decisions about student services.

Evidenced-Based School Counseling: Using Data To Write Meaningful Slo’S & Program Goals, Thomas Keller, Nick R. Abel

Nick R. Abel

Learning session conducted at the annual conference of the Indiana School Counseling Association, Indianapolis, IN. 2014, November.

Utilizing Data To Write Meaningful Slo’S That Pertain To School Counseling, Thomas Keller, Brandie M. Oliver, Nick R. Abel

Nick R. Abel

Learning session conducted at the Guidance & Counseling Workshop, Muncie, IN. 2014, April.

The Journey From Data To Qualitative Inductive Paper: Who Helps And How?, Špela Trefalt, Marya Besharov

Marya Besharov

How do qualitative inductive researchers move from raw data to publishable paper? Existing scholarly resources focus on how to analyze data more deeply and write it up to make a compelling contribution. Much less discussed but also critical is how researchers enlist others in the journey from data to paper – even when they are single authors of their work. Our chapter uncovers qualitative researchers’ helpers and the types of help they provide. Drawing on data from detailed accounts of single authored qualitative, inductive studies, we explain the role of qualitative and quantitative scholars, peers and hired professionals, field informants …

Determining Data Information Literacy Needs: A Study Of Students And Research Faculty, Jake R. Carlson, Michael Fosmire, Chris Miller, Megan R. Sapp Nelson

Michael Fosmire

Researchers increasingly need to integrate the disposition, management and curation of their data into their current workflows. However, it is not yet clear to what extent faculty and students are sufficiently prepared to take on these responsibilities. This paper articulates the need for a data information literacy program (DIL) to prepare students to engage in such an “e-research” environment. Assessments of faculty interviews and student performance in a geoinformatics course provide complementary sources of information, which are then filtered through the perspective of ACRL’s information literacy competency standards to produce a draft set of outcomes for a data information literacy …

A Multi-Institutional Project To Develop Discipline-Specific Data Literacy Instruction For Graduate Students, Sarah Wright, Michael Fosmire, Jon Jeffryes, Marianne Stowell Bracke, Brian Westra

Michael Fosmire

What data stewardship skills are needed by future scientists to fulfill their professional responsibilities and take advantage of opportunities in e-science? How can academic librarians contribute their expertise in information organization, dissemination and preservation to better serve modern science? With support from the Institute of Museum and Library Services (IMLS), four research libraries have formed a partnership to address these questions. The aims of the partnership are to identify the data stewardship skills, including data management and curation, needed by graduate students at the research discipline level, to identify trends that extend across the Science, Technology, Engineering, and Mathematics (STEM) …

Development Of A School Improvement Framework With Broad Application In Different Contexts, Elizabeth A. Hartnell-Young

Dr Elizabeth Hartnell-Young (Consultant)

School improvement has been a focus of attention in Australia for some time, but the publication of results in international testing such as PIRLS (Progress in International Reading Literacy Study), TIMSS (Trends in International Mathematics and Science Study) and PISA (Programme for International Student Assessment) in recent times has heightened awareness of comparisons between Australian students and their peers in other countries (eg. Thomson, Hillman, Wernert, Schmid, Buckley, and Munene, 2012). This paper describes the National School Improvement Tool (ACER, 2012) developed in Australia to support school and system improvement efforts.

Interpreting Data: Where’S The Evidence?, Katherine Dix

Dr Katherine Dix

Young Children's Mental Health Outcomes: Flinders University Evaluation Of An Australia-Wide 2-Year Mental Health Promotion Initiative In Early Childhood And Care Settings, Helen Askell-Williams, Rosalind Murray-Harvey, Phillip Slee, Katherine Dix, Grace Skrzypiec

Dr Katherine Dix

Scrutininzing Sexuality And Psychopathology: A Foucauldian Inspired Strategy For Qualitative Data Analysis, Valerie Harwood, Mary Lou Rasmussen

Valerie Harwood

No abstract provided.

Impact Of Different Item Location In Common-Item Test Equating, Luc Le

Dr Luc Tu Le

This study used real data and simulation data to investigate the impacts of different item location in common-item test equating. The real data set included responses of about 150 000 students to a generic skills test from a university survey in Columbia in 2011, where each student was assigned randomly to one of four linked test forms. Each form consisted of two of three Critical Reasoning clusters and two of three Quantitative Reasoning clusters (20 items in each cluster). The results of an analysis using Item Response Theory clearly showed that items could become more difficult when located towards the …

Turning Data Into Insight: A Data Driven Retention Model, Oscar T. Mcknight, Wenhui Jin

Oscar T McKnight Ph.D.

This poster session addresses how to turn data into insight; that is, when attempting to increasing college retention rates by Student Affairs assessment and intervention. The presentation emphasizes a four part strategy and culminates in demonstrating how to construct a predictive chart or a multivariate computer program; with an overview of both simple and multiple regression models. The ultimate goal is to design interventions that will upset the prediction model and increase student retention.

Part 1: Lessons Learnt From International Assessments, Dave Tout

David (Dave) Tout

In the first of two related articles, this article attempts to explain what can be learnt from international assessments of adult literacy and numeracy skills, and why there is value and crucial and important lessons for all educators, and others, from the work underpinning these empirically based research endeavours. Whilst some criticism of the surveys is understandable and to be expected, this often blinds readers and the LLN sector from reading between and behind the lines and seeing what value can be generated, gained and learned from such investigations, data and research. The article describes the theoretical frameworks and quality …

A School-Based Intervention To Promote Physical Activity Among Adolescent Girls: Rationale, Design, And Baseline Data From The Girls In Sport Group Randomised Controlled Trial, Anthony D. Okely, Wayne G. Cotton, David R. Lubans, Philip J. Morgan, Lauren M. Puglisi, Judy Miller, Jan Wright, Marijka Batterham, Louisa Peralta, Janine Perry

Dr Marijka Batterham

Background: Physical activity levels decline markedly among girls during adolescence. School-based interventions that are multi-component in nature, simultaneously targeting curricular, school environment and policy, and community links, are a promising approach for promoting physical activity. This report describes the rationale, design and baseline data from the Girls in Sport group randomised trial, which aims to prevent the decline in moderate-to-vigorous intensity physical activity (MVPA) among adolescent girls. Methods/design: A community-based participatory research approach and action learning framework are used with measurements at baseline and 18-month follow-up. Within each intervention school, a committee develops an action plan aimed at meeting the …

A School-Based Intervention To Promote Physical Activity Among Adolescent Girls: Rationale, Design, And Baseline Data From The Girls In Sport Group Randomised Controlled Trial, Anthony D. Okely, Wayne G. Cotton, David R. Lubans, Philip J. Morgan, Lauren M. Puglisi, Judy Miller, Jan Wright, Marijka Batterham, Louisa Peralta, Janine Perry

A. D. Okely

Background: Physical activity levels decline markedly among girls during adolescence. School-based interventions that are multi-component in nature, simultaneously targeting curricular, school environment and policy, and community links, are a promising approach for promoting physical activity. This report describes the rationale, design and baseline data from the Girls in Sport group randomised trial, which aims to prevent the decline in moderate-to-vigorous intensity physical activity (MVPA) among adolescent girls. Methods/design: A community-based participatory research approach and action learning framework are used with measurements at baseline and 18-month follow-up. Within each intervention school, a committee develops an action plan aimed at meeting the …

Maximum-Entropy Estimated Distribution Model For Classification Problems, L Tan, D Taniar

Dr Ling Tan

Classification is a fundamental problem in machine learning and data mining. This paper applies a stochastic optimization model to classification problems. The proposed maximum entropy estimated distribution model uses a probabilistic distribution to represent solution space, and a sampling technique to explore search space. This paper demonstrates the application of the proposed maximum entropy estimated distribution model to improve linear discriminant function and rule induction methods. In addition, this paper compares the proposed classification model with decision trees. It shows that the proposed model is preferable to decision tree C4.5 in the following cases: i) when prior distribution of classification …

Parametric Optimization In Data Mining Incorporated With Ga-Based Search, L Tan, D Taniar, K Smith

Dr Ling Tan

A number of parameters must be specified for a data-mining algorithm. Default values of these parameters are given and generally accepted as ‘good’ estimates for any data set. However, data mining models are known to be data dependent, and so are for their parameters. Default values may be good estimates, but they are often not the best parameter values for a particular data set. A tuned set of parameter values is able to produce a data-mining model of better classification and higher prediction accuracy. However parameter search is known to be expensive. This paper investigates GA-based heuristic techniques in a …