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Successful Integration of Data Science in Undergraduate Biostatistics Courses Using Cognitive Load Theory
Biostatistics courses are integral to many undergraduate biology programs. Such courses have often been taught using point-and-click software, but these programs are now seldom used by researchers or professional biologists. Instead, biology professionals typically use programming languages, such as...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Cell Biology
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6812565/ https://www.ncbi.nlm.nih.gov/pubmed/31622167 http://dx.doi.org/10.1187/cbe.19-02-0041 |
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author | Guzman, Laura Melissa Pennell, Matthew W. Nikelski, Ellen Srivastava, Diane S. |
author_facet | Guzman, Laura Melissa Pennell, Matthew W. Nikelski, Ellen Srivastava, Diane S. |
author_sort | Guzman, Laura Melissa |
collection | PubMed |
description | Biostatistics courses are integral to many undergraduate biology programs. Such courses have often been taught using point-and-click software, but these programs are now seldom used by researchers or professional biologists. Instead, biology professionals typically use programming languages, such as R, which are better suited to analyzing complex data sets. However, teaching biostatistics and programming simultaneously has the potential to overload the students and hinder their learning. We sought to mitigate this overload by using cognitive load theory (CLT) to develop assignments for two biostatistics courses. We evaluated the effectiveness of these assignments by comparing student cohorts who were taught R using these assignments (n = 146) with those who were taught R through example scripts or were instructed on a point-and-click software program (control, n = 181). We surveyed all cohorts and analyzed statistical and programming ability through students’ lab reports or final exams. Students who learned R through our assignments rated their programming ability higher and were more likely to put the usage of R as a skill in their curricula vitae. We also found that the treatment students were more motivated, less frustrated, and less stressed when using R. These results suggest that we can use CLT to teach challenging material. |
format | Online Article Text |
id | pubmed-6812565 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | American Society for Cell Biology |
record_format | MEDLINE/PubMed |
spelling | pubmed-68125652019-12-01 Successful Integration of Data Science in Undergraduate Biostatistics Courses Using Cognitive Load Theory Guzman, Laura Melissa Pennell, Matthew W. Nikelski, Ellen Srivastava, Diane S. CBE Life Sci Educ Article Biostatistics courses are integral to many undergraduate biology programs. Such courses have often been taught using point-and-click software, but these programs are now seldom used by researchers or professional biologists. Instead, biology professionals typically use programming languages, such as R, which are better suited to analyzing complex data sets. However, teaching biostatistics and programming simultaneously has the potential to overload the students and hinder their learning. We sought to mitigate this overload by using cognitive load theory (CLT) to develop assignments for two biostatistics courses. We evaluated the effectiveness of these assignments by comparing student cohorts who were taught R using these assignments (n = 146) with those who were taught R through example scripts or were instructed on a point-and-click software program (control, n = 181). We surveyed all cohorts and analyzed statistical and programming ability through students’ lab reports or final exams. Students who learned R through our assignments rated their programming ability higher and were more likely to put the usage of R as a skill in their curricula vitae. We also found that the treatment students were more motivated, less frustrated, and less stressed when using R. These results suggest that we can use CLT to teach challenging material. American Society for Cell Biology 2019 /pmc/articles/PMC6812565/ /pubmed/31622167 http://dx.doi.org/10.1187/cbe.19-02-0041 Text en © 2019 L. M. Guzman et al. CBE—Life Sciences Education © 2019 The American Society for Cell Biology. “ASCB®” and “The American Society for Cell Biology®” are registered trademarks of The American Society for Cell Biology. https://creativecommons.org/licenses/by-nc-sa/3.0/This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License. |
spellingShingle | Article Guzman, Laura Melissa Pennell, Matthew W. Nikelski, Ellen Srivastava, Diane S. Successful Integration of Data Science in Undergraduate Biostatistics Courses Using Cognitive Load Theory |
title | Successful Integration of Data Science in Undergraduate Biostatistics Courses Using Cognitive Load Theory |
title_full | Successful Integration of Data Science in Undergraduate Biostatistics Courses Using Cognitive Load Theory |
title_fullStr | Successful Integration of Data Science in Undergraduate Biostatistics Courses Using Cognitive Load Theory |
title_full_unstemmed | Successful Integration of Data Science in Undergraduate Biostatistics Courses Using Cognitive Load Theory |
title_short | Successful Integration of Data Science in Undergraduate Biostatistics Courses Using Cognitive Load Theory |
title_sort | successful integration of data science in undergraduate biostatistics courses using cognitive load theory |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6812565/ https://www.ncbi.nlm.nih.gov/pubmed/31622167 http://dx.doi.org/10.1187/cbe.19-02-0041 |
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