Dataset Information

Developing and Implementing Emergent Responding Training Systems With Available and Low-Cost Computer-Based Learning Tools: Some Best Practices and a Tutorial.

ABSTRACT: Systems and protocols based on emergent responding training have been demonstrated to be effective instructional tools for teaching a variety of skills to typically developing adult learners across a number of content areas in controlled research settings. However, these systems have yet to be widely adopted by instructors and are not often used in applied settings such as college classrooms or staff trainings. Proponents of emergent responding training systems have asserted that this failure might be because the protocols require substantial resources to develop, and there are no known manuals or guidelines to assist teachers or trainers with the development of the training systems. In order to assist instructors with the implementation of systems, we provide a brief summary of emergent responding training systems research; review the published computer-based training systems studies; present general guidelines for developing and implementing a training and testing system; and provide a detailed, task-analyzed written and visually supported manual/tutorial for educators and trainers using free and easily accessible computer-based learning tools and web applications. Educators and trainers can incorporate these methods and learning tools into their current curriculum and instructional designs to improve overall learning outcomes and training efficiency.

SUBMITTER: Blair BJ

PROVIDER: S-EPMC7314885 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Publications

Developing and Implementing Emergent Responding Training Systems With Available and Low-Cost Computer-Based Learning Tools: Some Best Practices and a Tutorial.

Blair Bryan J BJ Shawler Lesley A LA

Behavior analysis in practice 20191220 2

Systems and protocols based on emergent responding training have been demonstrated to be effective instructional tools for teaching a variety of skills to typically developing adult learners across a number of content areas in controlled research settings. However, these systems have yet to be widely adopted by instructors and are not often used in applied settings such as college classrooms or staff trainings. Proponents of emergent responding training systems have asserted that this failure mi ...[more]

PMID: 32647608

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Project description:MotivationPrincipal component analysis (PCA) of genetic data is routinely used to infer ancestry and control for population structure in various genetic analyses. However, conducting PCA analyses can be complicated and has several potential pitfalls. These pitfalls include (i) capturing linkage disequilibrium (LD) structure instead of population structure, (ii) projected PCs that suffer from shrinkage bias, (iii) detecting sample outliers and (iv) uneven population sizes. In this work, we explore these potential issues when using PCA, and present efficient solutions to these. Following applications to the UK Biobank and the 1000 Genomes project datasets, we make recommendations for best practices and provide efficient and user-friendly implementations of the proposed solutions in R packages bigsnpr and bigutilsr.ResultsFor example, we find that PC19-PC40 in the UK Biobank capture complex LD structure rather than population structure. Using our automatic algorithm for removing long-range LD regions, we recover 16 PCs that capture population structure only. Therefore, we recommend using only 16-18 PCs from the UK Biobank to account for population structure confounding. We also show how to use PCA to restrict analyses to individuals of homogeneous ancestry. Finally, when projecting individual genotypes onto the PCA computed from the 1000 Genomes project data, we find a shrinkage bias that becomes large for PC5 and beyond. We then demonstrate how to obtain unbiased projections efficiently using bigsnpr. Overall, we believe this work would be of interest for anyone using PCA in their analyses of genetic data, as well as for other omics data.Availability and implementationR packages bigsnpr and bigutilsr can be installed from either CRAN or GitHub (see https://github.com/privefl/bigsnpr). A tutorial on the steps to perform PCA on 1000G data is available at https://privefl.github.io/bigsnpr/articles/bedpca.html. All code used for this paper is available at https://github.com/privefl/paper4-bedpca/tree/master/code.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Developing and Implementing Emergent Responding Training Systems With Available and Low-Cost Computer-Based Learning Tools: Some Best Practices and a Tutorial.

Publications

Developing and Implementing Emergent Responding Training Systems With Available and Low-Cost Computer-Based Learning Tools: Some Best Practices and a Tutorial.

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