Literature DB >> 35623397

R Markdown as a dynamic interface for teaching: Modules from math and biology classrooms.

Kristine L Grayson1, Angela K Hilliker1, Joanna R Wares2.   

Abstract

Advancing technologies, including interactive tools, are changing classroom pedagogy across academia. Here, we discuss the R Markdown interface, which allows for the creation of partial or complete interactive classroom modules for courses using the R programming language. R Markdown files mix sections of R code with formatted text, including LaTeX, which are rendered together to form complete reports and documents. These features allow instructors to create classroom modules that guide students through concepts, while providing areas for coding and text response by students. Students can also learn to create their own reports for more independent assignments. After presenting the features and uses of R Markdown to enhance teaching and learning, we present examples of materials from two courses. In a Computational Modeling course for math students, we used R Markdown to guide students through exploring mathematical models to understand the principle of herd immunity. In a Data Visualization and Communication course for biology students, we used R Markdown for teaching the fundamentals of R programming and graphing, and for students to learn to create reproducible data investigations. Through these examples, we demonstrate the benefits of R Markdown as a dynamic teaching and learning tool.
Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Data visualization; Herd immunity; Pedagogy; R markdown; Teaching programming

Mesh:

Year:  2022        PMID: 35623397      PMCID: PMC9487201          DOI: 10.1016/j.mbs.2022.108844

Source DB:  PubMed          Journal:  Math Biosci        ISSN: 0025-5564            Impact factor:   3.935


  6 in total

1.  Programming tools: Adventures with R.

Authors:  Sylvia Tippmann
Journal:  Nature       Date:  2015-01-01       Impact factor: 49.962

2.  A note on the derivation of epidemic final sizes.

Authors:  Joel C Miller
Journal:  Bull Math Biol       Date:  2012-07-25       Impact factor: 1.758

3.  Comparison of beginning R students' perceptions of peer-made plots created in two plotting systems: a randomized experiment.

Authors:  Leslie Myint; Aboozar Hadavand; Leah Jager; Jeffrey Leek
Journal:  J Stat Educ       Date:  2019-12-23

4.  Promoting student metacognition.

Authors:  Kimberly D Tanner
Journal:  CBE Life Sci Educ       Date:  2012       Impact factor: 3.325

5.  Gender, Math Confidence, and Grit: Relationships with Quantitative Skills and Performance in an Undergraduate Biology Course.

Authors:  K M Flanagan; J Einarson
Journal:  CBE Life Sci Educ       Date:  2017       Impact factor: 3.325

6.  Getting Messy with Authentic Data: Exploring the Potential of Using Data from Scientific Research to Support Student Data Literacy.

Authors:  Melissa K Kjelvik; Elizabeth H Schultheis
Journal:  CBE Life Sci Educ       Date:  2019-06       Impact factor: 3.325

  6 in total

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