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      Perceptions, satisfactions, and performance of undergraduate students during Covid‐19 emergency remote teaching

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          Abstract

          Due to the Covid‐19 pandemic, the education system worldwide faced sudden and unforeseen challenges. Many academic institutions closed their doors, forcing both educators and students to transition to Emergency Remote Teaching (ERT) for the remainder of the semester. This transition eliminated hands‐on experiences, increased workload, and altered curricula. However, these aspects, as well as students' perceptions, study habits, and performance in response to ERT remain poorly documented. This contribution describes changes in the curriculum of an undergraduate cadaver‐based laboratory, and explores students' performance, self‐perceived learning, and overall satisfaction during this educational crisis. Online content delivery for this course included both asynchronous instruction and synchronous discussion sessions. While formative assessments remained the same, online spotter examinations included short answer, multiple choice, multiple answer, ordering, and true and false questions. Despite examination grades improving 20% during ERT, students reported lower levels of learning, confidence, and engagement with the course materials when compared to the face‐to‐face portion of the class. The most prevalent challenges identified by students were those related to the loss of access to cadaver‐based learning, including difficulty identifying and visualizing structures in three dimensions, and the loss of context and sensorial cues. Flexibility in taking examinations and learning the material at their own pace were recognized as positive outcomes of the ERT transition. While the resulting student perceptions and performances are unsurprising, they offer insight into the challenges of fostering a productive learning environment in a future threatened by epidemic outbreak and economic uncertainty.

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          Is Open Access

          Fitting Linear Mixed-Effects Models Using lme4

          Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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            The psychological impact of the COVID-19 epidemic on college students in China

            Highlights • Methods of guiding students to effectively and appropriately regulate their emotions during public health emergencies and avoid losses caused by crisis events have become an urgent problem for colleges and universities. Therefore, we investigated and analyzed the mental health status of college students during the epidemic for the following purposes. (1) To evaluate the mental situation of college students during the epidemic; (2) to provide a theoretical basis for psychological interventions with college students; and (3) to provide a basis for the promulgation of national and governmental policies.
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              Medical Student Education in the Time of COVID-19

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                Author and article information

                Contributors
                vhgonza@ku.edu
                Journal
                Anat Sci Educ
                Anat Sci Educ
                10.1002/(ISSN)1935-9780
                ASE
                Anatomical Sciences Education
                John Wiley and Sons Inc. (Hoboken )
                1935-9772
                1935-9780
                20 January 2022
                Jan-Feb 2022
                20 January 2022
                : 15
                : 1 ( doiID: 10.1002/ase.v15.1 )
                : 42-56
                Affiliations
                [ 1 ] Department of Ecology and Evolutionary Biology College of Liberal Arts and Sciences University of Kansas Lawrence Kansas USA
                [ 2 ] Undergraduate Biology Program College of Liberal Arts and Sciences University of Kansas Lawrence Kansas USA
                Author notes
                [*] [* ] Correspondence

                Dr. Victor H. Gonzalez, Department of Ecology and Evolutionary Biology, Haworth Hall, 1200 Sunnyside Ave., University of Kansas, Lawrence, KS 66045, USA.

                Email: vhgonza@ 123456ku.edu

                Author information
                https://orcid.org/0000-0002-4146-1634
                Article
                ASE2161
                10.1002/ase.2161
                9011711
                34859608
                3ea37a70-0a6c-4b30-976b-f978e118a1a8
                © 2021 American Association for Anatomy

                This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.

                History
                : 13 July 2021
                : 22 June 2020
                : 29 November 2021
                Page count
                Figures: 5, Tables: 4, Pages: 15, Words: 15827
                Funding
                Funded by: National Science Foundation , doi 10.13039/100000001;
                Award ID: DBI 1560389
                Categories
                Research Report
                Covid‐19 Articles
                Research Reports
                Custom metadata
                2.0
                January/February 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.4 mode:remove_FC converted:15.04.2022

                course value,distance education,face‐to‐face learning,self‐lead learning,student perceptions,student satisfaction,technology in education

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