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      When Online Courses Became the Student Union: Technologies for Peer Interaction and Their Association With Improved Outcomes During COVID-19

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          Abstract

          While a variety of learning technologies are presently available to facilitate student-to-student peer interactions and collaborative learning online, recent research suggests that students’ opportunities to interact with their peers were significantly reduced following the abrupt transition to remote instruction due to coronavirus disease. This raises concerns because peer interaction is known to be a key ingredient in effective online learning environments, and during remote instruction, the primary connection between a student and their identity as a member of a college community would have been online courses. In this study, we investigate whether and how collaborative technologies supported peer interaction, and students’ learning, during remote instruction. Specifically, we used results from a multicampus survey of students and instructors, as well as data from our online learning management system, to explore the use of collaborative tools at a large scale and their associations with student outcomes. Findings indicate that instructors, as was typical before the pandemic, generally favored individual learning activities over collaborative activities during campus closure. But in those situations where collaborative activities were present during remote instruction, triangulation analyses indicate that their use was related to improved performance as measured by instructors’ survey responses, by students’ performance in their courses, and by an increased sense of belonging among students.

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            Bayesian estimation for 2 groups provides complete distributions of credible values for the effect size, group means and their difference, standard deviations and their difference, and the normality of the data. The method handles outliers. The decision rule can accept the null value (unlike traditional t tests) when certainty in the estimate is high (unlike Bayesian model comparison using Bayes factors). The method also yields precise estimates of statistical power for various research goals. The software and programs are free and run on Macintosh, Windows, and Linux platforms. PsycINFO Database Record (c) 2013 APA, all rights reserved.
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              The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective.

              In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.
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                Author and article information

                Journal
                Technology, Mind, and Behavior
                American Psychological Association
                2689-0208
                January 5, 2022
                : 3
                : 1
                Affiliations
                [1]eLearning Research and Practice Lab, Pervasive Technology Institute, Indiana University
                [2]Department of Psychological and Brain Sciences, Indiana University
                [3]School of Education, Indiana University
                Author notes
                Special Collection Editors: Rachel Flynn and Fran Blumberg.
                Action Editor: Fran Blumberg was the action editor for this article.
                Acknowledgments: We appreciate the contributions and assistance of Julie Wernert, Tonya Miles, Erica Moore, Craig Stewart, Judy Ouimet, Todd Schmitz, and Maggie Ricci.
                Funding: This research was made possible by support from the Indiana University Office of the Vice President for Research and from Schmidt Futures, a philanthropic initiative cofounded by Eric and Wendy Schmidt.
                Disclosures: We affirm that there are no conflicts of interest, actual nor potential, related to the conduct of this study.
                Open Science Disclosures:

                The data are available at https://osf.io/vcg4d/

                The experiment materials are available at https://osf.io/vcg4d/

                [*] Benjamin A. Motz, eLearning Research and Practice Lab, Pervasive Technology Institute, Indiana University, 1320 East 10th Street, Bloomington, IN 47405, United States bmotz@indiana.edu
                Author information
                https://orcid.org/0000-0002-0379-2184
                https://orcid.org/0000-0001-5921-4984
                https://orcid.org/0000-0002-0565-6792
                Article
                2022-17931-001
                10.1037/tmb0000061
                1b0b29e1-ac12-4754-8fca-6a0a9d9b82ec
                © 2022 The Author(s)

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND). This license permits copying and redistributing the work in any medium or format for noncommercial use provided the original authors and source are credited and a link to the license is included in attribution. No derivative works are permitted under this license.

                History
                Categories
                INNOVATIONS IN REMOTE INSTRUCTION

                Education,Psychology,Vocational technology,Engineering,Clinical Psychology & Psychiatry
                peer interaction,computer-supported collaborative learning,learning analytics,educational technology,online discussion

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