Using an R shiny to enhance the learning experience of confidence intervals

Williams, Immanuel; Williams, Kelley Kim

doi:10.1111/test.12145

Cited by 12 publications

(9 citation statements)

References 1 publication

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“…Another example of an interactive online tool is the common online data analysis platform (CODAP), which has been integrated into the teaching of machine learning and other data science topics [28]. Interactive activities embedded in online applets facilitate students learning at their own pace, and self-discovery has been shown to improve students' understanding and ability to retain information [29,30].…”

Section: Introductionmentioning

confidence: 99%

Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module

et al. 2022

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Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, “Introduction to Ecological Forecasting,” at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students’ ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts.

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Section: Introductionmentioning

confidence: 99%

Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module

et al. 2022

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“…R-shiny applications have also been used as an additional resource when teaching challenging statistical concepts. Williams and Williams (2018) created an R-Shiny application to assist students in learning difficult statistical concepts such as confidence intervals. In an aim to improve the future development of R-Shiny applications to be used in the teaching of introductory statistics Gonzales et al ( 2018) conducted a qualitative study where the student's perspective of learning statistics with R-Shiny was as-sessed across ten R-Shiny applications developed for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Emergency Remote Teaching and Programme Intervention: Towards a Human-Machine Pedagogy Based on Interactive Learning Documents

Stiefenhofer¹,

Xie²,

Wildish-Jones³

2022

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Teaching restrictions, due to the COVID-19 pandemic, have rapidly shifted the global learning environment demanding educators to implement technology-based solutions in their instruction. In this case study, we discuss how Interactive Learning Documents (ILDs), based on R-Markdown and R-Shiny technologies can contribute to building student confidence in their transition from secondary to tertiary education under the conditions of Emergency Remote Teaching. We propose a human-machine based active learning pedagogy to be implemented in a stage one Introductory Statistics course delivered to social science students. Our results show that Interactive Learning Documents contribute to alleviating student's transition to university education. Three factors contribute to this result: 1) ILDs help students reduce statistics anxiety, 2) ILDs help learning by efficiently organising large quantities of complex learning material, and 3) ILDs effectively engage students by providing them with a variety of dynamic interactions with the subject.

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“…Teaching Statistics also has a rich history of publishing articles utilizing Shiny applications in various ways to illustrate statistical concepts [1,11,15]. This article is also accompanied by a Shiny application [2] available online at https://glow.shinyapps.io/dice/.…”

Section: Introductionmentioning

confidence: 99%

Bayesian explorations with dice

Berg

2021

Teaching Statistics

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The topic of Bayesian updating is explored using standard and non‐standard dice as an intuitive and motivating model. Details of calculating posterior probabilities for a discrete distribution are provided, offering a different view to P‐values. This article also includes the stars and bars counting technique, a powerful method of counting that is accessible to students who have been introduced to permutations and combinations. Supportive R code is included throughout, and a Shiny application accompanies the article allowing for an interactive exploration of the topics discussed.

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Using an R shiny to enhance the learning experience of confidence intervals

Cited by 12 publications

References 1 publication

Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module

Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module

Emergency Remote Teaching and Programme Intervention: Towards a Human-Machine Pedagogy Based on Interactive Learning Documents

Bayesian explorations with dice

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