Dordt College is a private, Christian, liberal arts college located in Sioux Center, Iowa, United States. It was founded in 1955 and is affiliated with the Christian Reformed Church in North America. The college name is a reference to the Synod of Dordt .Dordt annually enrolls about 1,300 students from more than 30 states, seven Canadian provinces, and 10 other countries, with a student-faculty ratio of 15:1. U.S. News and World Report has included Dordt in its America’s Best Colleges listing for 18 straight years , including a six top 10 rankings in the Midwest region’s Best Baccalaureate Colleges. In 2008 it was tied for #3 in the Midwest region.The college is committed to a Reformed, Christian perspective that embraces the Bible as the word of God. The college offers 90 programs of study that lead to Associate of Arts, Bachelor of Arts, Bachelor of Science in Engineering, Bachelor of Social Work, Bachelor of Science in Nursing and Master of Education degrees. Wikipedia.
Breems N.,Dordt College |
Basden A.,University of Salford
Computers in Human Behavior | Year: 2014
Computer procrastination is a complex problem that is under-researched. After identifying a number of key characteristics of it, we survey five existing fields of research that may contribute insights into this interdisciplinary problem, and demonstrate that none of these areas can provide satisfactory insight on their own. A philosophical framework for understanding computer use is introduced, and applied to a case study to demonstrate its potential in understanding the richness of computer procrastination. We then show how this framework can reveal the ways in which each of the existing fields is limited in its ability. The result is both an understanding of why existing research has not directly addressed this issue, and suggestions for a way forward for further research into computer procrastination. © 2013 Published by Elsevier Ltd.
Aslibekyan S.,University of Alabama at Birmingham |
Almeida M.,The Texas Institute |
Tintle N.,Dordt College
Genetic Epidemiology | Year: 2014
Pathway analysis, broadly defined as a group of methods incorporating a priori biological information from public databases, has emerged as a promising approach for analyzing high-dimensional genomic data. As part of Genetic Analysis Workshop 18, seven research groups applied pathway analysis techniques to whole-genome sequence data from the San Antonio Family Study. Overall, the groups found that the potential of pathway analysis to improve detection of causal variants by lowering the multiple-testing burden and incorporating biologic insight remains largely unrealized. Specifically, there is a lack of best practices at each stage of the pathway approach: annotation, analysis, interpretation, and follow-up. Annotation of genetic variants is inconsistent across databases, incomplete, and biased toward known genes. At the analysis stage insufficient statistical power remains a major challenge. Analyses combining rare and common variants may have an inflated type I error rate and may not improve detection of causal genes. Inclusion of known causal genes may not improve statistical power, although the fraction of explained phenotypic variance may be a more appropriate metric. Interpretation of findings is further complicated by evidence in support of interactions between pathways and by the lack of consensus on how to best incorporate functional information. Finally, all presented approaches warranted follow-up studies, both to reduce the likelihood of false-positive findings and to identify specific causal variants within a given pathway. Despite the initial promise of pathway analysis for modeling biological complexity of disease phenotypes, many methodological challenges currently remain to be addressed. © 2014 WILEY PERIODICALS, INC.
Liu K.,Harvard University |
Fast S.,St. Olaf College |
Zawistowski M.,University of Michigan |
Tintle N.L.,Dordt College
Genetic Epidemiology | Year: 2013
The wave of next-generation sequencing data has arrived. However, many questions still remain about how to best analyze sequence data, particularly the contribution of rare genetic variants to human disease. Numerous statistical methods have been proposed to aggregate association signals across multiple rare variant sites in an effort to increase statistical power; however, the precise relation between the tests is often not well understood. We present a geometric representation for rare variant data in which rare allele counts in case and control samples are treated as vectors in Euclidean space. The geometric framework facilitates a rigorous classification of existing rare variant tests into two broad categories: tests for a difference in the lengths of the case and control vectors, and joint tests for a difference in either the lengths or angles of the two vectors. We demonstrate that genetic architecture of a trait, including the number and frequency of risk alleles, directly relates to the behavior of the length and joint tests. Hence, the geometric framework allows prediction of which tests will perform best under different disease models. Furthermore, the structure of the geometric framework immediately suggests additional classes and types of rare variant tests. We consider two general classes of tests which show robustness to noncausal and protective variants. The geometric framework introduces a novel and unique method to assess current rare variant methodology and provides guidelines for both applied and theoretical researchers. © 2013 Wiley Periodicals, Inc.
Agency: NSF | Branch: Standard Grant | Program: | Phase: S-STEM:SCHLR SCI TECH ENG&MATH | Award Amount: 550.10K | Year: 2014
This PI team is working on two complementary fronts to increase the adoption of a new randomization-based curricula for introductory statistics that emphasizes: i) the core logic of inference using randomization-based methods alongside an intuitive, cyclical, active-learning pedagogy, and ii) the overall process of statistical investigations, from asking questions and collecting data through making inferences and drawing conclusions. The first front involves: a) conducting a series of twelve 1-4 day professional development workshops, and b) developing and supporting an online learning community to provide on-going support beyond the workshops. An important aspect of the intellectual merit of the project lies in the second front which involves the evaluation of widespread transferability of the model curriculum and research to deepen understanding of students attitudes, conceptual understanding, and learning trajectories within the curriculum. Together with the workshop participants, developers, and other faculty, the team is gathering a large and diverse set of attitudinal and conceptual assessment data from over 3000 students at a variety of institutions. In-depth qualitative and quantitative assessments of students developmental learning trajectories on key concepts are being carried out. The project is exercising broader impacts not only through its planned workshops, but also through coordination of its efforts through the Consortium for the Advancement of Undergraduate Statistics Education (CAUSE), which serves as the primary hub of activity for transformational efforts in statistics education.
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 248.65K | Year: 2013
Recent breakthroughs in genetic sequencing technology have set the stage for an unprecedented wide understanding of all microbial life. Previously, automated methods for the creation of genome-scale metabolic models (MMs) have been developed and applied by this group. These methods have since been applied to thousands of microbial genomes in freely available online software systems. While the MMs are yielding unprecedented insights into microbial metabolism, the next great challenge is to capture gene regulatory information in order to more accurately model the metabolic response of an organism to its environment. Anticipating this need, ongoing efforts by the group have set the stage for integrating a wide range of alternative data sources with the thousands of MMs. The group is now uniquely positioned to develop enhanced MMs through the integration of regulatory information into the models, yielding integrated metabolic, regulatory models (iMRMs). To date, little methodological effort has been directed toward the wide-scale development of iMRMs due, in part, to the lack of sufficient and integrated data for most organisms. This project will (1) develop new and improved iMRMs by addressing methodological weaknesses in current approaches, (2) develop methods to utilize iMRMs to predict conditions for wet-lab experiments to generate and test novel biological hypotheses, (3) develop novel approaches to use thousands of models to better understand metabolic and regulatory diversity, and (4) fully incorporate undergraduate and high school students in all aspects of the research. Thus, the project will substantially advance the capacity to construct integrated metabolic regulatory models through the development and evaluation of methods for the incorporation of regulatory data, produce tools for researchers to assess the diversity of existing gene expression data sets for their organisms of interest, validate proposed methods via targeted wet lab experiments, and address fundamental questions about metabolic and regulatory diversity across the microbial tree of life.
All methodological advancements will be integrated into an open-source software environment for modeling microbial life. At least 21 undergraduate and at least 60 high school students will be integrally involved in the research, providing them with training, experience and exposure to the interdisciplinary field of quantitative approaches for predictive systems biology.