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      Online teaching self-efficacy during COVID-19: Changes, its associated factors and moderators

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          Abstract

          Online teaching transition during COVID-19 school lockdown elicited challenges for teachers and schools across the globe. The existing literature on the impact of COVID-19 in the education sector is predominantly descriptive and focused on the difficulties faced by teachers during the process of transferring into online teaching, mainly in the higher education sector. This study adopted a mixed-method design to examine online teaching self-efficacy (TSE) during COVID-19, its associated factors and moderators. A sample of 351 Chinese school teachers retrospectively reported their online TSE at the beginning and end of COVID-19 school lockdown, out of which six were followed up for an in-depth interview. TSE for online instruction did not significantly increase (β = .014, p > 0.05) whereas that for technology application increased significantly (β = .231, p < 0.01). Lack of experience in online teaching, separation of teachers from students, school administrative process and unsatisfactory student academic performance were identified as the major associated factors. A moderation effect of adaptability and teacher burnout on the change in online TSE were examined, of which passion burnout was the only significant moderator toward the change in online TSE. The study thus concluded that teachers’ online TSE for technology application increased among Chinese teachers during COVID-19 school lockdown.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s10639-021-10486-3.

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          Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

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            Principles of Good Practice for the Translation and Cultural Adaptation Process for Patient-Reported Outcomes (PRO) Measures: report of the ISPOR Task Force for Translation and Cultural Adaptation.

            In 1999, ISPOR formed the Quality of Life Special Interest group (QoL-SIG)--Translation and Cultural Adaptation group (TCA group) to stimulate discussion on and create guidelines and standards for the translation and cultural adaptation of patient-reported outcome (PRO) measures. After identifying a general lack of consistency in current methods and published guidelines, the TCA group saw a need to develop a holistic perspective that synthesized the full spectrum of published methods. This process resulted in the development of Translation and Cultural Adaptation of Patient Reported Outcomes Measures--Principles of Good Practice (PGP), a report on current methods, and an appraisal of their strengths and weaknesses. The TCA Group undertook a review of evidence from current practice, a review of the literature and existing guidelines, and consideration of the issues facing the pharmaceutical industry, regulators, and the broader outcomes research community. Each approach to translation and cultural adaptation was considered systematically in terms of rationale, components, key actors, and the potential benefits and risks associated with each approach and step. The results of this review were subjected to discussion and challenge within the TCA group, as well as consultation with the outcomes research community at large. Through this review, a consensus emerged on a broad approach, along with a detailed critique of the strengths and weaknesses of the differing methodologies. The results of this review are set out as "Translation and Cultural Adaptation of Patient Reported Outcomes Measures--Principles of Good Practice" and are reported in this document.
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              Is Open Access

              A brief introduction to mixed effects modelling and multi-model inference in ecology

              The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.
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                Author and article information

                Contributors
                kang.ma1@hdr.mq.edu.au
                Journal
                Educ Inf Technol (Dordr)
                Educ Inf Technol (Dordr)
                Education and Information Technologies
                Springer US (New York )
                1360-2357
                1573-7608
                10 March 2021
                : 1-23
                Affiliations
                [1 ]GRID grid.1004.5, ISNI 0000 0001 2158 5405, School of Education, , Macquarie University, ; Sydney, NSW 2109 Australia
                [2 ]GRID grid.411587.e, ISNI 0000 0001 0381 4112, School of Science, , Chongqing University of Posts and Telecommunications, ; Chongqing, China
                [3 ]GRID grid.266842.c, ISNI 0000 0000 8831 109X, School of Education, , University of Newcastle, ; Newcastle, Australia
                Author information
                http://orcid.org/0000-0002-2600-7150
                https://orcid.org/0000-0002-7378-6302
                https://orcid.org/0000-0002-3237-2541
                Article
                10486
                10.1007/s10639-021-10486-3
                7946405
                33723481
                7e757729-b62b-4b3b-9bcb-515b4a61d364
                © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 1 December 2020
                : 22 February 2021
                Categories
                Article

                online teaching,teacher self-efficacy,covid-19,china
                online teaching, teacher self-efficacy, covid-19, china

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