r package design of experiments

Current state of R packages for the design of experiments

Your analytical toolkit matters very little if the data are no good. Ideally you want to know to how the data were collected before delving into the analysis of the data; better yet, get involved before the collection of data and design its collection. In this post I explore some of the top downloaded R packages for the design of experiments and analysis of experimental data.

Monash University

February 3, 2021

Data collection

As many know, it doesn’t matter how good your analytical tools is if your data are rubbish. This sentiment is often captured in the expression “garbage in, garbage out”. It’s something we all seem to know but there is still a tendency for many of us to place a greater focus on the analysis 1 . This is perhaps all natural given that a potential for discovery is just so much more exciting than ensuring the quality of the collected data.

So what is considered as good quality data? A lack of error in the data? Data containing enough range of variables and sample size for the downstream analysis? Giving an explicit definition of a good quality data is a fraught exercise, but if you know how the data were collected then you can better perform the initial data analysis ( Chatfield 1985 ) to weed out (or fix) potential poor quality data. This step will likely get more value out of the data than fitting complex models to poor quality data.

Better than knowing how the data were collected, if you can design the collection of data so that it’s optimised for the purpose of the analysis 2 , then you can potentially get even a better value out of your data. Not all data collection starts with an explicit analytical plan though. Furthermore, you may have very little control of how the data are collected. Often these are observational data or making a secondary use of experimental data . This article will focus on data collection of an experiment where you have some control of the collection process.

Experimental data

All experiments are conducted with some objective in mind. This could be that a scientist may wish to test their hypothesis, a manufacturer wants to know which manufacturing process is better or a researcher wants to understand some cause-and-effect relationships. A characteristic part of an experiment is that the experimenter has control over some explanatory variables. In a comparative experiment , the control is over the allocation of treatments to subjects. Designing an experiment in the statistics discipline usually focus on this allocation, although it’s important to keep in mind that there are other decision factors in an experiment.

Data that are collected from experiments are what we refer to as experimental data . Because it was collected with some objective in mind followed by some data collection plan, experimental data are often thought of to be better quality than observational data. But then again if you can’t quantify the quality of data, you can’t really tell. Certain scientific claims (e.g. causation, better treatment) can only be substantiated by experiments and so experimental data is held to a higher standard in general.

Design and analysis of experiments

There are all together 83 R-packages in the CRAN Task View of Design of Experiments & Analysis of Experimental Data as of 2022-09-18. 3 I’m going to refer these packages as DoE packages , although there are some packages in the mix that are more about the analysis of experimental data rather than the design of experiments and there are some packages that are missing in the list (e.g.  DeclareDesign ). The DoE packages make up about 0.4% of the 18,592 packages available in CRAN.

The DoE packages don’t include survey design. These instead belong to the CRAN Task View of Official Statistics & Survey Methodology which contains 122 packages. While some surveys are part of an experimental study, most often they generate observational data.

Below I have a number of different analysis for these DoE packages. If you push the button on the top right corner of this article, you can toggle the display for the code or alternatively you can have a look at the source Rmd document.

Bigram of DoE package titles and descriptions

Table @ref(tab:bigram-title) shows the most common bigrams in the title of the DoE packages. It’s perhaps not surprising but the words “optimal design” and “experimental design” are the top. It’s also likely that the words “design of experiments” appears often but because this is a bigram (two consecutive words) so it doesn’t appear. You might then wonder if that’s the case words like “design of” or “of experiments” should make an appearance, however “of” is a stop word and these are filtered out otherwise unwanted bigrams come up on the top.

There are couple of words like “clinical trial” and “dose finding” that suggests applications in medical experiments, as well as “microarray experiment” that suggests application in bioinformatics.

The title alone might be too succinct for text analysis so I also had a look at the most common bigrams in the description of the DoE packages as shown in Table @ref(tab:bigram-desc). The counts in Table @ref(tab:bigram-desc) (and also Table @ref(tab:bigram-title)) is across the DoE packages. To be more clear, even if the bigram is mentioned multiple times within the description, it’s only counted once per package. This removes the inflation of the counts due to one package mentioning the same bigram over and over again.

Again not surprisingly “experimental design” and “optimal design” comes on top in the DoE package descriptions. The words “graphical user” and “user interface” implies that the trigram “graphical user interface” was probably common.

Network of DoE package imports and dependencies

Figure @ref(fig:doe-network) shows the imports and dependency between the DoE packages. We can see here that DoE.wrapper imports a fair number of DoE packages that results in the major network cluster see in Figure @ref(fig:doe-network). AlgDesign and DoE.base are imported into four other DoE packages so form an important base in the DoE world.

(ref:network) The network of imports and dependency among DoE packages alone. Each node represents a DoE package. DoE packages with no imports or dependency on other DoE packages are excluded. Each arrow represents the relationship between the packages such that the package on the tail is used by package on the head of the arrow.

CRAN download logs

Figure @ref(fig:download-hist) shows the distribution of the total download counts over the last 5 years 4 of the DoE packages. This graph doesn’t take into account that some DoE packages may only have been on CRAN in the last 5 years so the counts are in favour of DoE packages that’s been on CRAN longer.

(ref:hist) Histogram of the total download count over last 5 years of the DoE packages.

Top 5 DoE packages

The top 5 downloaded DoE packages at the time of this writing are AlgDesign , lhs , DiceDesign , DoE.base , and FrF2 . You can see the download counts in Figure @ref(fig:download-barplot).

(ref:barplot) The above barplot shows the total downloads of the top 5 downloaded DoE packages from the period 2017-09-18 to 2022-09-16.

We can have a look at further examination of the top 5 DoE packages by looking at the daily download counts as shown in Figure @ref(fig:download-barplot). The download counts are the raw values and these include downloads by CRAN mirror and bots. There is a noticeable spike when there is an update to the CRAN package. This is partly because when there is a new version of the package, when you install other packages that depend or import it then R will prompt you to install the new version. This means that the download counts are inflated and to some extent you can artificially boost them by making regular CRAN updates. The adjustedcranlogs ( Morgan-Wall 2017 ) makes a nice attempt to adjust the raw counts based on a certain heuristic. I didn’t use it since the adjustment is stochastic and I appear to have hit a bug .

(ref:timeplot) The above plot shows the daily downloads of the top 5 downloaded DoE packages from the period 2017-09-18 to 2022-09-16. The vertical dotted bar corresponds to the date that a new version of the corresponding package was released on CRAN.

Here we have a closer look at the functions of the top 5 downloaded DoE packages below ordered by their download counts.

• AlgDesign CRAN GitHub Wheeler ( 2019 ) Algorithmic Experimental Design Originally written by Bob Wheeler but Jerome Braun have taken over maintenance of the package.
• agricolae CRAN de Mendiburu ( 2020 ) Statistical Procedures for Agricultural Research Written and maintained by Felipe de Mendiburu
• lhs CRAN GitHub Carnell ( 2020 ) Latin Hypercube Samples Written and maintained by Rob Carnell
• ez CRAN GitHub Lawrence ( 2016 ) Easy Analysis and Visualization of Factorial Experiments Written and maintained by Michael A. Lawrence
• DoE.base CRAN Grömping ( 2018 ) Full Factorials, Orthogonal Arrays and Base Utilities for DoE Packages Written and maintained by Ulrike Groemping.

Before we look at the packages, let’s set a seed so we can reproduce the results.

To start off, we begin with the most downloaded DoE package, AlgDesign . The examples below are taken directly from the vignette of the AlgDesign package .

You can create a balanced incomplete block design using the optBlock function. It’s using an optimal design framework where the default criterion is D criterion and the implied model is given in the first argument.

AlgDesign also includes helper functions to generate a factorial structure.

This can be an input to specify the design using another function, say with optFederov which uses Federov’s exchange algorithm to generate the design.

If you want to further randomise within blocks, you can pass the above result to optBlock .

agricolae is motivated by agricultural applications although the designs are applicable across a variety of fields.

The functions to create the design all begin with the word “design.” and the names of the functions are remnant of the name of the experimental design. E.g. design.rcbd generates a Randomised Complete Block Design and design.split generates a Split Plot Design.

Rather than going through each of the functions, I’ll just show one. The command below generates a balanced incomplete block design with 7 treatments of block size 3. This the same design structure as the first example for AlgDesign . What do you think of the input and output?

More examples are given in the agricolae tutorial .

The lhs package is completely different to the previous two packages. It implements methods for creating and augmenting Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples. The treatment variables here are the parameters and are continuous. In the example below, there are 10 parameters were 30 samples will be drawn from.

lhs provides a number of methods to find the optimal design each with their own criteria.

This is mainly focussed on the analysis of experimental data but some functions such as ezDesign is useful for viewing the experimental structure.

DoE.base provides utility functions for the special class design and as seen in Figure @ref(fig:doe-network), DoE.base is used by four other DoE packages that is maintained also by Prof. Dr. Ulrike Grömping .

DoE.base contains functions to generate factorial designs easily.

It also contains functions to create orthogonal array designs.

If you need to further randomise within a specified block, you can do this using rerandomize.design .

So those were the top 5 DoE packages. The API of the packages are quite distinct. The object that it outputs can vary from a matrix to a list. DoE might be a dull area for many but it’s quite important for the downstream analysis. Perhaps if many of us talk more about it, it may help invigorate the area!

At least from my teaching experience, statistics subjects are primary about the analysis and most research grants I’ve seen are about an analytical method. The analytical focus is reflected also in the R packages; there are 1,907 R-packages on CRAN with the word “analysis” in the title as opposed to 287 R-packages with the word “design” in its title. ↩︎

Keeping in mind though that your analysis plan may change once you actually have collected data. This is quite common in the analysis of plant breeding trials since some spatial variation only become apparent only after the data collection. ↩︎

I originally had a webscrapping error where I didn’t remove duplicate entries so numbers presented at TokyoR and SSA Webinar had the wrong numbers. ↩︎

As of 2022-09-18. ↩︎

skpr is an open source design of experiments suite for generating and evaluating optimal designs in R. Here is a sampling of what skpr offers:

• Generates and evaluates D, I, A, Alias, E, T, and G optimal designs, as well as user-defined custom optimality criteria.
• Supports generation and evaluation of split/split-split/…/N-split plot designs.
• Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses.
• Provides an extensible framework for the user to evaluate Monte Carlo power using their own libraries.
• Includes a Shiny graphical user interface, skprGUI, that auto-generates the R code used to create and evaluate the design to improve ease-of-use and enhance reproducibility.

Installation

• gen_design() generates optimal designs from a candidate set, given a model and the desired number of runs.
• eval_design() evaluates power parametrically for linear models, for normal and split-plot designs.
• eval_design_mc() evaluates power with a Monte Carlo simulation, for linear and generalized linear models. This function also supports calculating power for split-plot designs using REML.
• eval_design_survival_mc() evaluates power with a Monte Carlo simulation, allowing the user to specify a point at which the data is censored.
• eval_design_custom_mc() allows the user to import their own libraries and use the Monte Carlo framework provided by skpr to calculate power.
• calculate_power_curves() provides an interface to automate the generation and evaluation of designs to create power versus sample size and effect size curves.
• skprGUI() opens up the GUI in either RStudio or an external browser.

If addition, the package offers two functions to generate common plots related to designs:

• plot_correlations() generates a color map of correlations between variables.
• plot_fds() generates the fraction of design space plot for a given design.

skprGUI() provides an graphical user interface to access all of the main features of skpr. An interactive tutorial is provided to familiarize the user with the available functionality. Type skprGUI() to begin. Screenshots:

Experimental Design and Process Optimization with R

Gerhard Krennrich

1 Introduction

The present document is a short and elementary course on the Design of Experiments (DoE) and empirical process optimization with the open-source Software R . The course is self-contained and does not assume any preknowledge in statistics or mathematics beyond high school level. Statistical concepts will be introduced on an elementary level and made tangible with R-code and R-graphics based on simulated and real world data. So, then, what is DoE and why should the reader become familiar with the concepts of DoE? Very briefly, DoE is the science of varying many experimental parameters in a systematic way to gain insight on how to further improve and optimize these parameters. Chapter 2 will show how and why multidimensional DoE techniques are superiour to the classical “one-dimensional” optimization approach. Chapter 6 will demonstrate why and how DoE can be combined with optimization. Finally, the use of DoE and optimization will be practically demonstrated in chapter 7 for improving the performance of a catalytic system. Historically, Experimental Design started as a branch of statistics in the early years of the 20 th century and has meanwhile grown into a mature method with a plethora of applications in the experimental sciences. Consequently, there are many good and comprehensive books available about DoE, some of which we will make frequent reference to in the present text, namely (George E.P. Box, Norman R. Draper 1987 ) , (D.C. Montgomery 2013 ) and (G.E.P. Box, W.G. Hunter, J.S. Hunter 2005 ) . A more recent text with emphasis on the use of R in conjuction with DoE is (John Lawson 2015 ) . Linear models are comprehensively covered, e.g., by the text book (A. Sen, M. Srivastava 1990 ) . A general, however fairly technical text on linear and nonlinear statistical model building is the excellent book (T. Hastie, R. Tibshirani, J. Friedman 2009 ) . (J.G. Kalbfleisch 1985 ) is a smooth introduction into statistics, probability and statistical inference. The present text draws on these books and on many years of experience as a statistical consultant in the chemical industry. Most examples in this course are therefore taken from applications and optimization projects in the chemical sciences. The primarily intended readers of this document are chemists and engineers entrusted with empirical optimization in research and development. However, the presented methods and concepts are fairly generic and scientist working in other areas such as biology or the medical sciences might benefit from the text. As to software, R, probably together with Phyton, is the only open-source software which combines the whole spectrum of DoE and optimization with the flexibility of a powerful script language that allows any kind of data pre- and postprocessing within one software environment. That makes, in my opinion, R superior to many commercial GUI based tools which often buy userfriendlyness at the expense of flexibility.

1.1 How to install R

The R-software can be downloaded free of charge from the R repository CRAN

An IDE ( I ntegrated D evelopment E nvironment) is reqired for smoothly working with R. An IDE allows editing, running and debugging of R code and managing programm in- and output. In principle any IDE can be used but we recommend R-Studio as the de-facto standard.

Get R-Studio IDE

The R-introduction at CRAN is a concise introduction into the R-language. A short R-introduction

1.2 Some remarks on how to read the present text

This document is not an introduction into the R language, rather the document follows the philosophy of “learning by doing”. In this spirit the above mentioned text R-introduction is recommended as a first reference together with the present R examples on DoE and optimization. As it is usually easier to modify existing code than writing code from scratch, it is hoped that the R-examples in this course will help learning both R and DoE more rapidly. The course is divided into seven chapters. There is, however, one stand-alone chapter, chapter 5, which can be skipped by those readers not explicitly dealing with mixture problems. The final chapter 7 is a published, (Siebert M., Krennrich G., Seibicke M., Siegle A.F., Trapp O. 2019 ) , real-world example combining many elements of DoE and optimization for improving the performance of a catalytic system. This application should encourage readers to use these powerful methods for the sake of their own projects.

A. Sen, M. Srivastava. 1990. Regression Analysis, Theory, Methods and Applications . 1st ed. Springer-Verlag, New York.

D.C. Montgomery. 2013. Design and Analysis of Experiments . 8th ed. John Wiley & Sons Inc.

G.E.P. Box, W.G. Hunter, J.S. Hunter. 2005. Statistics for Experimenters: Design, Innovation, and Discovery . 2nd ed. John Wiley & Sons, Hoboken.

George E.P. Box, Norman R. Draper. 1987. Empirical Model-Building and Response Surfaces . 1st ed. John Wiley & Sons.

J.G. Kalbfleisch. 1985. Probability and Statistical Inference, Vol 1&2 . 2nd ed. Springer.

John Lawson. 2015. Design and Analysis of Experiments with R . 1st ed. Chapman & Hall.

Siebert M., Krennrich G., Seibicke M., Siegle A.F., Trapp O. 2019. “Identifying High-Performance Catalytic Conditions for Carbon Dioxide Reduction to Dimethoxymethane by Multivariate Modelling.” Chemical Science 10:45. https://pubs.rsc.org/en/content/articlelanding/2019/sc/c9sc04591k#!divAbstract .

T. Hastie, R. Tibshirani, J. Friedman. 2009. The Elements of Statistical Learning . 2nd ed. Springer-Verlag.

Design of Experiments with R

• First Online: 01 January 2012

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• Emilio L. Cano 4 ,
• Javier M. Moguerza 4 &
• Andrés Redchuk 4  

Part of the book series: Use R! ((USE R,volume 36))

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Design of experiments (DoE) is one of the most important tools in the Six Sigma methodology. It is the essence of the Improve phase and the basis for the design of robust processes. An adequate use of DoE will lead to the improvement of a process, but a bad design can result in wrong conclusions and engender the opposite of the desired effect: inefficiencies, higher costs, and less competitiveness. In this chapter, we introduce the foundations of DoE and describe the essential functions in R to perform it and analyze its results. We will describe two-level factorial designs using a representative example of how DoE should be used to achieve the improvement of a process in a Six Sigma way. The chapter is not intended as a thorough review of DoE. The idea is to introduce a simple model in an intuitive way. For more technical or advance training a number of references are given at the end of the chapter.

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Cano, E.L., Moguerza, J.M., Redchuk, A. (2012). Design of Experiments with R. In: Six Sigma with R. Use R!, vol 36. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3652-2_11

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FielDHub is an R package/shiny design of experiments (DOE) app that aids in the creation of traditional, un-replicated, augmented and partially-replicated designs applied to agriculture, plant breeding, forestry, animal and biological sciences.

For more details and examples of all functions present in the FielDHub package. Please, go to https://didiermurillof.github.io/FielDHub/reference/index.html .

This is a basic example which shows you how to launch the app:

Diagonal Arrangement Example

A project needs to test 280 genotypes in a field containing 16 rows and 20 columns of plots. In this example, these 280 genotypes are divided among three different experiments. In addition, four checks are included in a systematic diagonal arrangement across experiments to fill 40 plots representing 12.5% of the total number of experimental plots. An option to include filler plots is also available for fields where the number of experimental plots does not equal the number of available field plots.

The figure above shows a map of an experiment randomized along with multiple experiments (three) and checks on diagonals. Distinctively colored check plots are replicated throughout the field in a systematic diagonal arrangement.

The figure above shows the layout for the three experiments in the field.

Using the FielDHub function diagonal_arrangement()

To illustrate using FielDHub to build experimental designs through R code, the design produced in the R Shiny interface described above can also be created using the function diagonal_arrangement() in the R script below. Note, that to obtain identical results, users must include the same random seed in the script as was used in the Shiny app. In this case, the random seed is 1249.

Users can print the returned values from diagonal_arrangement() as follow,

First 12 rows of the fieldbook,

Users can plot the layout design from diagonal_arrangement() using the function plot() as follows,

In the figure, salmon, green, and blue shade the blocks of unreplicated experiments, while distinctively colored check plots are replicated throughout the field in a systematic diagonal arrangement.

The main difference between using the FielDHub Shiny app and using the standalone function diagonal_arrangement() is that the standalone function will allocate filler only if it is necessary, while in Shiny App, users can customize the number of fillers if it is needed. In cases where users include fillers, either between or after experiments, the Shiny app is preferable for filling and visualizing all field plots.

To see more examples, go to https://didiermurillof.github.io/FielDHub/articles/diagonal_arrangement.html

Partially Replicated Design Example

Partially replicated designs are commonly employed in early generation field trials. This type of design is characterized by replication of a portion of the entries, with the remaining entries only appearing once in the experiment. As an example, considered a field trial with 288 plots containing 75 entries appearing two times each, and 138 entries only appearing once. This field trials is arranged in a field of 16 rows by 18 columns.

In the figure above, green plots contain replicated entries, and the other plots contain entries that only appear once.

Using the FielDHub function partially_replicated()

Instead of using the Shiny FielDHub app, users can use the standalone FielDHub function partially_replicated() . The partially replicated layout described above can be produced through scripting as follows. As noted in the previous example, to obtain identical results between the script and the Shiny app, users need to use the same random seed, which, in this case, is 77.

Users can print returned values from partially_replicated() as follows,

Users can plot the layout design from partially_replicated() using the function plot() as follows,

To see more examples, please go to https://didiermurillof.github.io/FielDHub/articles/partially_replicated.html

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R package that seeks better design of experiments

HAOYU-LI/UniDOE

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Package: unidoe, author: aijun zhang, haoyu li and zebin yang, date: 22/08/2017.

R(>= 3.4.1), Rtools(>=34)

Rcpp (>= 0.12.12)

Linking To:

Introduction:.

UniDOE is a R package, which implements an efficient stochastic evolutionary(SE) algorithm to search for design of experiment . Computational procedures are mainly achieved by c++ so that the calculation speed is greatly boosted. Users can either download and install from binary source package or install from github directly using devtools, of which details are illustrated below. This package is distributed in the hope that it will be useful, but without any warranty.

How to install:

At first, Make sure you are using R(>=3.4.1). Typing 'version' in R command line can retrieve related information, e.g.:

Output should show corresponding R version and architecture of your current platform:

(Update: Feb, 2018) Install from CRAN:

UniDOE is currently published to CRAN, users can conveniently install it from R Command line:

This package will be modified and updated in CRAN directly. This github repository may not be the newest version.

Install from github:

Download and install Rcpp(>=0.12.12) package if you haven't installed or updated it to >=0.12.12 version.

In R command-line:

Git clone this repostory to your local machine. After that, you can install UniDOE from local files:

Choose UniDOE_0.1.1.zip to install Or install it from GUI.

It's more convenient to install UniDOE using devtools. At first, make sure you installed devtools.

Then install UniDOE from github:

Useful links:

• Experimental design - Intro to design of experiments
• License - License for this project
• Jupyter Notebook 62.0%
• Python 0.3%

UniDOE Uniform Design of Experiments

• DesignEval: Evaluate design in terms of criteria
• DesignPairPlot: Draw pair plot for design of experiments
• DesignQuery: Evaluate design in terms of criteria
• GenAUD: Generating Augmented Uniform Design of Experiments
• GenAUD_MS: Generating sequential Uniform Design of Experiments using...
• GenLP: Generating uniform designs by level permutation
• GenUD: Generating Uniform Design of Experiments
• GenUD_MS: Generating Uniform Design of Experiments using diffrent...
• LHD_CD2: LHD Tables under CD2
• LHD_MD2: LHD Tables under MD2
• UD_CD2: UD Tables under CD2
• UD_MD2: UD Tables under MD2
• unidoe-internal: UniDOE internal functions
• UniTracePlot: Draw discrepancy value trace in optimization process.
• Browse all...

UniDOE: Uniform Design of Experiments

Efficient procedures for constructing uniform design of experiments under various space-filling criteria. It is based on a stochastic and adaptive threshold accepting algorithm with flexible initialization, adaptive threshold, and stochastic evolution. The package may also construct the augmented uniform designs in a sequential manner.

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A First Course in Design and Analysis of Experiments

This book by Gary W. Oehlert was first published in 2000 by W. H. Freeman. As of summer 2010, it has gone out of print. Curiously, I still like this book and would prefer to continue using it in my teaching; some of my colleagues feel the same way. And since the copyright has reverted to me, we can do that.

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You may download A First Course in Design and Analysis of Experiments by clicking here (1.9 MB PDF).

Download Draft Second Edition

The designation "draft" is literal. All of the books are under active development, and you will be able to tell where I stopped revising and assembled this distribution file.

The PDF versions of the text include links to (some of) the material in the Examples supplement. The "local" version of the text tries to link to the local files in the RExamples subdirectory, and the "web" version of the text tries to link to files at www.stat.umn.edu/~gary/book/RExamples. The advantage of the local version is that you can use it without a network connection, but some platforms (iOS in particular) seem to make it very hard to use local files.

Whether these links work seems to be hit or miss. For example, I use a Mac, and the links work when the text is viewed using Adobe Reader, but they don't work when using Preview. On Linux, the local links didn't work using Xpdf but the web links did, while both worked under "Document Viewer". I really don't know what happens under iOS, Android, or Windows, but I'm guessing that some of it will work.

Download R Package (with data) for Second Edition

The R-package cfcdae (Companion to A First Course in Design and Analysis of Experiments) contains all of the data from the draft second edition of the book along with a handful of useful functions. (Many of these functions have analogs in other packages, they just do things the way I like.)

You may download cfcdae by clicking here

This package is in source format. Download the package and save the file into a place where R can find it (e.g., your home directory or the desktop). Start R, set the working directory to that location (e.g., use setwd(), and then use install.packages("oehlert_1..4-11.tar.gz",repos=NULL,type="source") (The repos=NULL says not to find it online but to look for the package in the local files; the type="source" tells R the file format.) Once the package is installed, you can do library(cfcdae) to load the library.

Download Data from first edition

All of the individual text data sets accessible via the web as above are also available in a single zip archive

Russ Lenth at the University of Iowa has also provided an R package that include the data sets from the book. oehlert_1.02.tar.gz Download the package and save the file into a place where R can find it (e.g., your home directory or the desktop). Start R, set the working directory to that location (e.g., use setwd(), and then use install.packages("oehlert_1.02.tar.gz",repos=NULL,type="source") (The repos=NULL says not to find it online but to look for the package in the local files; the type="source" tells R the file format.) Once the package is installed, you can do library(oehlert) from within R to load all of the data. At that point, the command pr17.4 should give you problem 4 from chapter 17.

Note that the data set names, variable names, and variable codings in oehlert.Rdata and the direct-web-accessible data may not be the same.

oehlert-data.sas An Ascii text file in SAS format. Oehlert-SAS.zip A zip archive of individual SAS data files. Again, thanks to Russ Lenth for these files.

Single text file This is in the MacAnova matread format.

Both the SAS data format and the MacAnova matread format are plain text files. You can download either and then cut/paste if you need to put the data into another format.

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Design of experiments with mixtures and their analysis with r.

Posted on June 30, 2022 by R in the Lab in R bloggers | 0 Comments

Using R for design and analysis of results for experiments with mixtures.

“Mixtures are absolutely everywhere you look. Anything you can combine is a mixture.” (chem4kids.com)

All the code and data in this post are available in the repository: Design of Experiments with Mixtures and their Analysis with R

What are experimental designs with mixtures?

These are designs aimed at determining the effect of the proportion of different components of a mixture on one or more response variables.

We must emphasize that we are referring to the proportions of the different components in the mixture and not to their absolute amount. That is, it is the proportion that determines the effect.

This type of design has application in the formulation of many products such as beverages, foods, fuels, paints, etc.

In an experimental design of mixtures, the sum of the proportions of each component is equal to 1:

And the limits of the proportion of each component must be between 0 and 1:

In a practical problem, calculating the proportions of each component is straightforward. For example, suppose that the sum of three components in a soda equals 2.5 g, and the respective amounts of each component are 1, 1, and 0.5 g. The ratio for the first and second ingredient is 1/2.5 = 0.4, and the ratio for the third ingredient is 0.5/2.5 = 0.2. Thus 0.4 + 0.4 + 0.2 = 1.

An experimental design of mixtures will help us determine the proportions of each component to produce the best flavor or to reduce some undesirable physical property in the liquid, for example.

Types of mixture designs and their generation in R

In this post I will focus on two types of mix designs: simplex-lattice and simplex-centroid. The generation of these designs is simple with the mixexp package .

Simplex-lattice design

The simplex-lattice design considers q components and allows fitting a model of order m to the experimental data. To generate a design with 3 components of order 3 we use the function SLD() as follows:

It should be noted that it is not necessary to specify the levels (proportions) of each component, as these are automatically determined by the ratio:

If the proportions of each line are added together, the result will be equal to 1. In addition, for three components it is possible to use the DesignPoints() function to visualize the experimental region of the experiment:

In this figure, the three vertices correspond to pure mixtures (formed by a single ingredient), the three sides or edges represent binary mixtures that have only two of the three components. The interior points of the triangle represent the ternary mixtures in which the three ingredients are different from zero.

Finally, the design can be exported to our working folder with the function write.csv() :

Simplex-centroid design

If predictions are to be made within the experimental region, it is important to include centroid points within the experimental region. The simplex-centroid design includes all intermediate mixtures between components. The SCD() function is used to generate it:

By visualizing the experimental region with three components, it becomes much clearer what we mean by intermediate mixtures:

Mixing designs with component constraints

It is normal that due to technical or economic constraints, for example, the proportion of one or more components is restricted to a shape limit:

It is possible to generate designs considering the constraints for each component with the Xvert() function:

It is also possible to visualize the experimental subregion:

Analysis of the results of a mix design

For the example analysis, I will use the data published in Performance of reduced fat-reduced salt fermented sausage with added microcrystalline cellulose, resistant starch and oat fiber using the simplex design . In this study, the effect of the proportion of three ingredients on different characteristics of fermented sausages was determined. In this case I will only focus the analysis on one of the response variables (hardness).

Data import

As usual, the first step in the analysis is to import our data into R:

Model adjustment

You can use the lm() function to adjust a complete model or, for the same purpose, the MixModel() function of the mixexp package:

Note that these models did not include the mean or intercept. Due to the restriction of the sum of components is equal to 1, the parameters in the model are not unique. Basically, the model without mean eliminates the problem of dependence between the coefficients. As we will see later, the interpretation of each coefficient and the hypothesis testing related to each must be done in a special way for this type of design.

Model coefficients, their interpretation and determination coefficients

The summary() function will display a complete report with the coefficients of the model we previously selected, as well as the coefficients of determination:

In general, the coefficients in this type of model are interpreted as follows:

• Coefficients of individual components. They do not measure the overall effect of component xi , but only estimate the value of the response at the vertex of the simplex. If these coefficients are not significant, it does not mean that the effect of the individual component is not important, so hypothesis tests on them are usually ignored.
• Coefficients of double interactions. If the sign of this coefficient is positive, there is synergy between the components; if it is negative, there is antagonism between them.
• Triple interaction coefficients. Quantifies the effect of the ternary mixture within the simplex.

The result report can be easily saved with the capture.output() function:

step() function to improve the model

In order to improve the coefficients of determination or simplify the model, sometimes non-significant terms are eliminated. This can be done somewhat subjectively by trial and error by eliminating one or more terms and then comparing with the full model. R also offers a systematic way to do the above using the step() function, this function uses the Akaike information criterion iteratively to simplify and/or improve the coefficients of determination of the model.

The function can display a large number of results depending on the number of iterations it makes to simplify the model, so in this example I will directly save the results in a text file:

Subsequently, we only need to adjust the simplified model:

By displaying a summary of results it can be seen that there is not a big difference between the coefficients of determination of this model and the full model. However, the smaller number of terms may have some practical advantage depending on the problem:

Lack-of-fit test

Another way to evaluate the quality of the model fit, if there is more than one repetition for any of the treatments, is by means of a lack-of-fit test . This can be done directly with the pureErrorAnova() function of the alr3 package:

For the simplified model:

In this test, if the p-value obtained for Lack of fit is greater than 0.05, or at the significance level established by the experimenter, it can be concluded that the model fits the data adequately. Note how with the full model we came close to rejecting the hypothesis of lack of fit, while with the simplified model the situation improved somewhat.

Visualization of the simplified model in two dimensions

It is possible to make a contour plot with the fitted model, only for the case of three components in the mixture:

The ModelPlot() function is also included in the mixexp package.

The graph can be exported in png format, for example, as follows:

Mixture effect plot

Another way to plot the results is by using an effect plot for the components of the mixture. This two-dimensional plot can be useful if you have more than three components in the mixture. To do this we can use the ModelEff() function included in mixexp :

ModelEff() displays the components in the same order as specified in the fitted model, so x1 corresponds to MCC, x2 corresponds to RS and x3 corresponds to OF. This plot starts with a reference mixture (usually the center of the experimental region) and shows how the response changes as one of the components increases or decreases in the mixture; when one of the components changes, the rest increase or decrease proportionally. The disadvantage of ModelEff() is that only complete, not simplified, models can be used to make the plot.

The effect graph can be exported in the same way as the contour graph:

If the reader would like to consult more examples of analysis with the mixexp package, please check the document at the following link: Mixture Experiments in R Using mixexp .

Very good! That’s all for this post, thank you very much for visiting this blog.

Juan Pablo Carreón Hidalgo 🤓

[email protected] https://github.com/jpch26

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Statistics > Applications

Title: cosimmr: an r package for fast fitting of stable isotope mixing models with covariates.

Abstract: The study of animal diets and the proportional contribution that different foods make to their diets is an important task in ecology. Stable Isotope Mixing Models (SIMMs) are an important tool for studying an animal's diet and understanding how the animal interacts with its environment. We present cosimmr, a new R package designed to include covariates when estimating diet proportions in SIMMs, with simple functions to produce plots and summary statistics. The inclusion of covariates allows for users to perform a more in-depth analysis of their system and to gain new insights into the diets of the organisms being studied. A common problem with the previous generation of SIMMs is that they are very slow to produce a posterior distribution of dietary estimates, especially for more complex model structures, such as when covariates are included. The widely-used Markov chain Monte Carlo (MCMC) algorithm used by many traditional SIMMs often requires a very large number of iterations to reach convergence. In contrast, cosimmr uses Fixed Form Variational Bayes (FFVB), which we demonstrate gives up to an order of magnitude speed improvement with no discernible loss of accuracy. We provide a full mathematical description of the model, which includes corrections for trophic discrimination and concentration dependence, and evaluate its performance against the state of the art MixSIAR model. Whilst MCMC is guaranteed to converge to the posterior distribution in the long term, FFVB converges to an approximation of the posterior distribution, which may lead to sub-optimal performance. However we show that the package produces equivalent results in a fraction of the time for all the examples on which we test. The package is designed to be user-friendly and is based on the existing simmr framework.

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Effects of pecha kucha presentation pedagogy on nursing students’ presentation skills: a quasi-experimental study in Tanzania

• Setberth Jonas Haramba 1 ,
• Walter C. Millanzi 1 &
• Saada A. Seif 2  

BMC Medical Education volume  24 , Article number:  952 ( 2024 ) Cite this article

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Introduction

Ineffective and non-interactive learning among nursing students limits opportunities for students’ classroom presentation skills, creativity, and innovation upon completion of their classroom learning activities. Pecha Kucha presentation is the new promising pedagogy that engages students in learning and improves students’ speaking skills and other survival skills. It involves the use of 20 slides, each covering 20 seconds of its presentation. The current study examined the effect of Pecha Kucha’s presentation pedagogy on presentation skills among nursing students in Tanzania.

The aim of this study was to establish comparative nursing student’s presentation skills between exposure to the traditional PowerPoint presentations and Pecha Kucha presentations.

The study employed an uncontrolled quasi-experimental design (pre-post) using a quantitative research approach among 230 randomly selected nursing students at the respective training institution. An interviewer-administered structured questionnaire adopted from previous studies to measure presentation skills between June and July 2023 was used. The study involved the training of research assistants, pre-assessment of presentation skills, training of participants, assigning topics to participants, classroom presentations, and post-intervention assessment. A linear regression analysis model was used to determine the effect of the intervention on nursing students’ presentation skills using Statistical Package for Social Solution (SPSS) version 26, set at a 95% confidence interval and 5% significance level.

Findings revealed that 63 (70.87%) participants were aged ≤ 23 years, of which 151 (65.65%) and 189 (82.17%) of them were males and undergraduate students, respectively. Post-test findings showed a significant mean score change in participants’ presentation skills between baseline (M = 4.07 ± SD = 0.56) and end-line (M = 4.54 ± SD = 0.59) that accounted for 0.4717 ± 0.7793; p  < .0001(95%CI) presentation skills mean score change with a medium effect size of 0.78. An increase in participants’ knowledge of Pecha Kucha presentation was associated with a 0.0239 ( p  < .0001) increase in presentation skills.

Pecha Kucha presentations have a significant effect on nursing students’ presentation skills as they enhance inquiry and mastery of their learning content before classroom presentations. The pedagogical approach appeared to enhance nursing students’ confidence during the classroom presentation. Therefore, there is a need to incorporate Pecha Kucha presentation pedagogy into nursing curricula and nursing education at large to promote student-centered teaching and learning activities and the development of survival skills.

Trial registration

It was not applicable as it was a quasi-experimental study.

Peer Review reports

The nursing students need to have different skills acquired during the learning process in order to enable them to provide quality nursing care and management in the society [ 1 ]. The referred nursing care and management practices include identifying, analyzing, synthesizing, and effective communication within and between healthcare professionals [ 1 ]. Given an increasing global economy and international competition for jobs and opportunities, the current traditional classroom learning methods are insufficient to meet such 21st - century challenges and demands [ 2 ]. The integration of presentation skills, creativity, innovation, collaboration, information, and media literacy skills helps to overcome the noted challenges among students [ 2 , 3 , 4 ]. The skills in question constitute the survival skills that help the students not only for career development and success but also for their personal, social and public quality of life as they enable students to overcome 21st challenges upon graduation [ 2 ].

To enhance the nursing students’ participation in learning, stimulating their presentation skills, critical thinking, creativity, and innovation, a combination of teaching and learning pedagogy should be employed [ 5 , 6 , 7 , 8 ]. Among others, classroom presentations, group discussions, problem-based learning, demonstrations, reflection, and role-play are commonly used for those purposes [ 5 ]. However, ineffective and non-interactive learning which contribute to limited presentation skills, creativity, and innovation, have been reported by several scholars [ 9 , 10 , 11 ]. For example, poor use and design of student PowerPoint presentations led to confusing graphics due to the many texts in the slides and the reading of about 80 slides [ 12 , 13 , 14 ]. Indeed, such non-interactive learning becomes boring and tiresome among the learners, and it is usually evidenced by glazing eyes, long yawning, occasional snoring, the use of a phone and frequent trips to the bathroom [ 12 , 14 ].

With an increasing number of nursing students in higher education institutions in Tanzania, the students’ traditional presentation pedagogy is insufficient to stimulate their presentation skills. They limit nursing student innovation, creativity, critical thinking, and meaningful learning in an attempt to solve health challenges [ 15 , 16 ].These hinder nursing students ability to communicate effectively by being able to demonstrate their knowledge and mastery of learning content [ 17 , 18 ]. Furthermore, it affects their future careers by not being able to demonstrate and express their expertise clearly in a variety of workplace settings, such as being able to present at scientific conferences, participating in job interviews, giving clinic case reports, handover reports, and giving feedback to clients [ 17 , 18 , 19 ].

Pecha Kucha presentation is a new promising approach for students’ learning in the classroom context as it motivates learners’ self-directed and collaborative learning, learner creativity, and presentation skills [ 20 , 21 , 22 ]. It encourages students to read more materials, enhances cooperative learning among learners, and is interesting and enjoyable among students [ 23 ].

Pecha Kucha presentation originated from the Japanese word “ chit chat , ” which represents the fast-paced presentation used in different fields, including teaching, marketing, advertising, and designing [ 24 , 25 , 26 ]. It involves 20 slides, where each slide covers 20 s, thus making a total of 6 min and 40 s for the whole presentation [ 22 ]. For effective learning through Pecha Kucha presentations, the design and format of the presentation should be meaningfully limited to 20 slides and targeted at 20 s for each slide, rich in content of the presented topic using high-quality images or pictures attuned to the content knowledge and message to be delivered to the target audiences [ 14 , 16 ]. Each slide should contain a primordial message with well-balanced information. In other words, the message should be simple in the sense that each slide should contain only one concept or idea with neither too much nor too little information, thus making it easy to be grasped by the audience [ 14 , 17 , 19 ].

The “true spirit” of Pecha Kucha is that it mostly consists of powerful images and meaningful specific text rather than the text that is being read by the presenter from the slides, an image, and short phrases that should communicate the core idea while the speaker offers well-rehearsed and elaborated comments [ 22 , 28 ]. The presenter should master the subject matter and incorporate the necessary information from classwork [ 14 , 20 ]. The audience’s engagement in learning by paying attention and actively listening to the Pecha Kucha presentation was higher compared with that in traditional PowerPoint presentations [ 29 ]. The creativity and collaboration during designing and selecting the appropriate images and contents, rehearsal before the presentation, and discussion after each presentation made students satisfied by enjoying Pecha Kucha presentations compared with traditional presentations [ 21 , 22 ]. Time management and students’ self-regulation were found to be significant through the Pecha Kucha presentation among the students and teachers or instructors who could appropriately plan the time for classroom instruction [ 22 , 23 ].

However, little is known about Pecha Kucha presentation in nursing education in Sub-Saharan African countries, including Tanzania, since there is insufficient evidence for the research(s) that have been published on the description of its effects on enhancing students’ presentation skills. Thus, this study assessed the effect of Pecha Kucha’s presentation pedagogy on enhancing presentation skills among nursing students. In particular, the study largely focused on nursing students’ presentation skills during the preparation and presentation of the students’ assignments, project works, case reports, or field reports.

The study answered the null hypothesis H 0  = H 1, which hypothesized that there is no significant difference in nursing students’ classroom presentation skills scores between the baseline and end-line assessments. The association between nursing students’ presentation skills and participants’ sociodemographic characteristics was formulated and analyzed before and after the intervention. This study forms the basis for developing new presentation pedagogy among nursing students in order to stimulate effective learning and the development of presentation skills during the teaching and learning process and the acquisition of 21st - century skills, which are characterized by an increased competitive knowledge-based society due to changing nature and technological eruptions.

The current study also forms the basis for re-defining classroom practices in an attempt to enhance and transform nursing students’ learning experiences. This will cultivate the production of graduates nurses who will share their expertise and practical skills in the health care team by attending scientific conferences, clinical case presentations, and job interviews in the global health market. To achieve this, the study determined the baseline and end-line nursing students’ presentation skills during the preparation and presentation of classroom assignments using the traditional PowerPoint presentation and Pecha Kucha presentation format.

Methods and materials

This study was conducted in health training institutions in Tanzania. Tanzania has a total of 47 registered public and private universities and university colleges that offer health programs ranging from certificate to doctorate degrees [ 24 , 25 ]. A total of seven [ 7 ] out of 47 universities offer a bachelor of science in nursing, and four [ 4 ] universities offer master’s to doctorate degree programs in nursing and midwifery sciences [ 24 , 26 ]. To enhance the representation of nursing students in Tanzania, this study was conducted in Dodoma Municipal Council, which is one of Tanzania’s 30 administrative regions [ 33 ]. Dodoma Region has two [ 2 ] universities that offer nursing programs at diploma and degree levels [ 34 ]. The referred universities host a large number of nursing students compared to the other five [ 5 ] universities in Tanzania, with traditional students’ presentation approaches predominating nursing students’ teaching and learning processes [ 7 , 32 , 35 ].

The two universities under study include the University of Dodoma and St. John’s University of Tanzania, which are located in Dodoma Urban District. The University of Dodoma is a public university that provides 142 training programs at the diploma, bachelor degree, and master’s degree levels with about 28,225 undergraduate students and 724 postgraduate students [ 26 , 27 ]. The University of Dodoma also has 1,031 nursing students pursuing a Bachelor of Science in Nursing and 335 nursing students pursuing a Diploma in Nursing in the academic year 2022–2023 [ 33 ]. The St. John’s University of Tanzania is a non-profit private university that is legally connected with the Christian-Anglican Church [ 36 ]. It has student enrollment ranging from 5000 to 5999 and it provides training programs leading to higher education degrees in a variety of fields, including diplomas, bachelor degrees, and master’s degrees [ 37 ]. It hosts 766 nursing students pursuing a Bachelor of Science in Nursing and 113 nursing students pursuing a Diploma in Nursing in the academic year 2022–2023 [ 30 , 31 ].

Study design and approach

An uncontrolled quasi-experimental design with a quantitative research approach was used to establish quantifiable data on the participants’ socio-demographic profiles and outcome variables under study. The design involved pre- and post-tests to determine the effects of the intervention on the aforementioned outcome variable. The design involved three phases, namely the baseline data collection process (pre-test via a cross-sectional survey), implementation of the intervention (process), and end-line assessment (post-test), as shown in Fig.  1 [ 7 ].

A flow pattern of study design and approach

Target population

The study involved nursing students pursuing a Diploma in nursing and a bachelor of science in nursing in Tanzania. The population was highly expected to demonstrate competences and mastery of different survival and life skills in order to enable them to work independent at various levels of health facilities within and outside Tanzania. This cohort of undergraduate nursing students also involved adult learners who can set goals, develop strategies to achieve their goals, and hence achieve positive professional behavioral outcomes [ 7 ]. Moreover, as per annual data, the average number of graduate nursing students ranges from 3,500 to 4,000 from all colleges and universities in the country [ 38 ].

Study population

The study involved first- and third-year nursing students pursuing a Diploma in Nursing and first-, second-, and third-year nursing students pursuing a Bachelor of Science in Nursing at the University of Dodoma. The population had a large number of enrolled undergraduate nursing students, thus making it an ideal population for intervention, and it approximately served as a good representation of the universities offering nursing programs [ 11 , 29 ].

Inclusion criteria

The study included male and female nursing students pursuing a Diploma in nursing and a bachelor of science in nursing at the University of Dodoma. The referred students included those who were registered at the University of Dodoma during the time of study. Such students live on or off campus, and they were not exposed to PK training despite having regular classroom attendance. This enhanced enrollment of adequate study samples from each study program, monitoring of study intervention, and easy control of con-founders.

Exclusion criteria

All students recruited in the study were assessed at baseline, exposed to a training package and obtained their post-intervention learning experience. None of the study participants, who either dropped out of the study or failed to meet the recruitment criteria.

Sample size determination

A quasi-experimental study on Pecha Kucha as an alternative to traditional PowerPoint presentations at Worcester University, United States of America, reported significant student engagement during Pecha Kucha presentations compared with traditional PowerPoint presentations [ 29 ]. The mean score for the classroom with the traditional PowerPoint presentation was 2.63, while the mean score for the Pecha Kucha presentation was 4.08. This study adopted the formula that was used to calculate the required sample size for an uncontrolled quasi-experimental study among pre-scholars [ 39 ]. The formula is stated as:

Where: Zα was set at 1.96 from the normal distribution table.

Zβ was set at 0.80 power of the study.

Mean zero (π0) was the mean score of audiences’ engagement in using PowerPoint presentation = 2.63.

Mean one (π1) was the mean score of audience’s engagement in using Pecha Kucha presentation = 4.08.

Sampling technique

Given the availability of higher-training institutions in the study area that offer undergraduate nursing programs, a simple random sampling technique was used, whereby two cards, one labelled “University of Dodoma” and the other being labelled “St. Johns University of Tanzania,” were prepared and put in the first pot. The other two cards, one labelled “yes” to represent the study setting and the other being labelled “No” to represent the absence of study setting, were put in the second pot. Two research assistants were asked to select a card from each pot, and consequently, the University of Dodoma was selected as the study setting.

To obtain the target population, the study employed purposive sampling techniques to select the school of nursing and public health at the University of Dodoma. Upon arriving at the School of Nursing and Public Health of the University of Dodoma, the convenience sampling technique was employed to obtain the number of classes for undergraduate nursing students pursuing a Diploma in Nursing and a Bachelor of Science in Nursing. The study sample comprised the students who were available at the time of study. A total of five [ 5 ] classes of Diploma in Nursing first-, second-, and third-years and Bachelor of Science in Nursing first-, second-, and third-years were obtained.

To establish the representation for a minimum sample from each class, the number of students by sex was obtained from each classroom list using the proportionate stratified sampling technique (sample size/population size× stratum size) as recommended by scholars [ 40 ]. To recruit the required sample size from each class by gender, a simple random sampling technique through the lottery method was employed to obtain the required sample size from each stratum. During this phase, the student lists by gender from each class were obtained, and cards with code numbers, which were mixed with empty cards depending on the strata size, were allocated for each class and strata. Both labeled and empty cards were put into different pots, which were labeled appropriately by their class and strata names. Upon arriving at the specific classroom and after the introduction, the research assistant asked each nursing student to pick one card from the respective strata pot. Those who selected cards with code numbers were recruited in the study with their code numbers as their participation identity numbers. The process continued for each class until the required sample size was obtained.

To ensure the effective participation of nursing students in the study, the research assistant worked hand in hand with the facilitators and lecturers of the respective classrooms, the head of the department, and class representatives. The importance, advantages, and disadvantages of participating in the study were given to study participants during the recruitment process in order to create awareness and remove possible fears. During the intervention, study participants were also given pens and notebooks in an attempt to enable them to take notes. Moreover, the bites were provided during the training sessions. The number of participants from each classroom and the sampling process are shown in Fig.  2 [ 7 ].

Flow pattern of participants sampling procedures

Data collection tools

The study adapted and modified the students’ questionnaire on presentation skills from scholars [ 20 , 23 , 26 , 27 , 28 , 29 ]. The modification involved rephrasing the question statement, breaking down items into specific questions, deleting repeated items that were found to measure the same variables, and improving language to meet the literacy level and cultural norms of study participants.

The data collection tool consisted of 68 question items that assessed the socio-demographic characteristics of the study participants and 33 question items rated on a five-point Likert scale, which ranges from 5 = strongly agree, 4 = agree, 3 = not sure, 2 = disagree, and 1 = strongly disagree. The referred tool was used to assess the students’ skills during the preparation and presentation of the assignments using the traditional PowerPoint presentation and Pecha Kucha presentation formats.

The students’ assessment specifically focused on the students’ ability to prepare the presentation content, master the learning content, share presentation materials, and communicate their understanding to audiences in the classroom context.

Validity and reliability of research instruments

Validity of the research instrument refers to whether the instrument measures the behaviors or qualities that are intended to be measured, and it is a measure of how well the measuring instrument performs its function [ 41 ]. The structured questionnaire, which intends to assess the participants’ presentation skills was validated for face and content validity. The principal investigator initially adapted the question items for different domains of students’ learning when preparing and presenting their assignment in the classroom.

The items were shared and discussed by two [ 2 ] educationists, two [ 2 ] research experts, one [ 1 ] statistician, and supervisors in order to ensure clarity, appropriateness, adequacy, and coverage of the presentation skills using Pecha Kucha presentation format. The content validity test was used until the saturation of experts’ opinions and inputs was achieved. The inter-observer rating scale on a five-point Likert scale ranging from 5-points = very relevant to 1-point = not relevant was also used.

The process involved addition, input deletion, correction, and editing for relevance, appropriateness, and scope of the content for the study participants. Some of the question items were broken down into more specific questions, and new domains evolved. Other question items that were found to measure the same variables were also deleted to ease the data collection and analysis. Moreover, the grammar and language issues were improved for clarity based on the literacy level of the study participants.

Reliability of the research instruments refers to the ability of the research instruments or tools to provide similar and consistent results when applied at different times and circumstances [ 41 ]. This study adapted the tools and question items used by different scholars to assess the impact of PKP on student learning [ 12 , 15 , 18 ].

To ensure the reliability of the tools, a pilot study was conducted in one of the nursing training institutions in order to assess the complexity, readability, clarity, completeness, length, and duration of the tool. Ambiguous and difficult (left unanswered) items were modified or deleted based on the consensus that was reached with the consulted experts and supervisor before subjecting the questionnaires to a pre-test.

The study involved 10% of undergraduate nursing students from an independent geographical location for a pilot study. The findings from the pilot study were subjected to explanatory factor analysis (Set a ≥ 0.3) and scale analysis in order to determine the internal consistency of the tools using the Cronbach alpha of ≥ 0.7, which was considered reliable [ 42 , 43 , 44 ]. Furthermore, after the data collection, the scale analysis was computed in an attempt to assess their internal consistency using SPPSS version 26, whereby the Cronbach alpha for question items that assessed the participants’ presentation skills was 0.965.

Data collection method

The study used the researcher-administered questionnaire to collect the participants’ socio-demographic information, co-related factors, and presentation skills as nursing students prepare and present their assignments in the classroom. This enhanced the clarity and participants’ understanding of all question items before providing the appropriate responses. The data were collected by the research assistants in the classroom with the study participants sitting distantly to ensure privacy, confidentiality, and the quality of the information that was provided by the research participants. The research assistant guided and led the study participants to answer the questions and fill in information in the questionnaire for each section, domain, and question item. The research assistant also collected the baseline information (pre-test) before the intervention, which was then compared with the post-intervention information. This was done in the first week of June 2023, after training and orientation of the research assistant on the data collection tools and recruitment of the study participants.

Using the researcher-administered questionnaire, the research assistant also collected the participants’ information related to presentation skills as they prepared and presented their given assignments after the intervention during the second week of July 2023. The participants submitted their presentations to the principle investigator and research assistant to assess the organization, visual appeal and creativity, content knowledge, and adherence to Pecha Kucha presentation requirements. Furthermore, the evaluation of the participants’ ability to share and communicate the given assignment was observed in the classroom presentation using the Pecha Kucha presentation format.

Definitions of variables

Pecha kucha presentation.

It refers to a specific style of presentation whereby the presenter delivers the content using 20 slides that are dominated by images, pictures, tables, or figures. Each slide is displayed for 20 s, thus making a total of 400 s (6 min and 40 s) for the whole presentation.

Presentation skills in this study

This involved students’ ability to plan, prepare, master learning content, create presentation materials, and share them with peers or the audience in the classroom. They constitute the learning activities that stimulate creativity, innovation, critical thinking, and problem-solving skills.

Measurement of pecha kucha preparation and presentation skills

The students’ presentation skills were measured using the four [ 4 ] learning domains. The first domain constituted the students’ ability to plan and prepare the presentation content. It consisted of 17 question items that assessed the students’ ability to gather and select information, search for specific content to be presented in the classroom, find out the learning content from different resources, and search for literature materials for the preparation of the assignment using traditional PowerPoint presentations and Pecha Kucha formats. It also aimed to ascertain a deeper understanding of the contents or topic, learning ownership and motivation to learn the topics with clear understanding and the ability to identify the relevant audience, segregate, and remove unnecessary contents using the Pecha Kucha format.

The second domain constituted the students’ mastery of learning during the preparation and presentation of their assignment before the audience in the classroom. It consisted of six [ 6 ] question items that measured the students’ ability to read several times, rehearse before the classroom presentation, and practice the assignment and presentation harder. It also measures the students’ ability to evaluate the selected information and content before their actual presentation and make revisions to the selected information and content before the presentation using the Pecha Kucha format.

The third domain constituted the students’ ability to prepare the presentation materials. It consisted of six [ 6 ] question items that measured the students’ ability to organize the information and contents, prepare the classroom presentation, revise and edit presentation resources, materials, and contents, and think about the audience and classroom design. The fourth domain constituted the students’ ability to share their learning. It consisted of four [ 4 ] question items that measured the students’ ability to communicate their learning with the audience, present a new understanding to the audience, transfer the learning to the audience, and answer the questions about the topic or assignment given. The variable was measured using a 5-point Likert scale. The average scores were computed for each domain, and an overall mean score was calculated across all domains. Additionally, an encompassing skills score was derived from the cumulative scores of all four domains, thus providing a comprehensive evaluation of the overall skills level.

Implementation of intervention

The implementation of the study involved the training of research assistants, sampling of the study participants, setting of the venue, pre-assessment of the students’ presentation skills using traditional PowerPoint presentations, training and demonstration of Pecha Kucha presentations to study participants, and assigning the topics to study participants. The implementation of the study also involved the participants’ submission of their assignments to the Principal Investigator for evaluation, the participants’ presentation of their assigned topic using the Pecha Kucha format, post-intervention assessment of the students’ presentation skills, data analysis, and reporting [ 7 ]. The intervention involved Principal Investigator and two [ 2 ] trained research assistants. The intervention in question was based on the concept of multimedia theory of cognitive learning (MTCL) for enhancing effective leaning in 21st century.

Training of research assistants

Two research assistants were trained with regard to the principles, characteristics, and format of Pecha Kucha presentations using the curriculum from the official Pecha Kucha website. Also, research assistants were oriented to the data collection tools and methods in an attempt to guarantee the relevancy and appropriate collection of the participants’ information.

Schedule and duration of training among research assistants

The PI prepared the training schedule and venue after negotiation and consensus with the research assistants. Moreover, the Principle Investigator trained the research assistants to assess the learning, learn how to collect the data using the questionnaire, and maintain the privacy and confidentiality of the study participants.

Descriptions of interventions

The intervention was conducted among the nursing students at the University of Dodoma, which is located in Dodoma Region, Tanzania Mainland, after obtaining their consent. The participants were trained regarding the concepts, principles, and characteristics of Pecha Kucha presentations and how to prepare and present their assignments using the Pecha Kucha presentation format. The study participants were also trained regarding the advantages and disadvantages of Pecha Kucha presentations. The training was accompanied by one example of an ideal Pecha Kucha presentation on the concepts of pressure ulcers. The teaching methods included lecturing, brainstorming, and small group discussion. After the training session, the evaluation was conducted to assess the participants’ understanding of the Pecha Kucha conceptualization, its characteristics, and its principles.

Each participant was given a topic as an assignment from the fundamentals of nursing, medical nursing, surgical nursing, community health nursing, mental health nursing, emergency critical care, pediatric, reproductive, and child health, midwifery, communicable diseases, non-communicable diseases, orthopedics and cross-cutting issues in nursing as recommended by scholars [ 21 , 38 ]. The study participants were given 14 days for preparation, rehearsal of their presentation using the Pecha Kucha presentation format, and submission of the prepared slides to the research assistant and principle investigator for evaluation and arrangement before the actual classroom presentation. The evaluation of the participants’ assignments involved the number of slides, quality of images used, number of words, organization of content and messages to be delivered, slide transition, duration of presentation, flow, and organization of slides.

Afterwards, each participant was given 6 min and 40 s for the presentation and 5 min to 10 min for answering the questions on the topic presented as raised by other participants. An average of 4 participants obtained the opportunity to present their assignments in the classroom every hour. After the completion of all presentations, the research assistants assessed the participant’s presentation skills using the researcher-administered questionnaire. The collected data were entered in SPSS version 26 and analyzed in an attempt to compare the mean score of participants’ presentation skills with the baseline mean score. The intervention sessions were conducted in the selected classrooms, which were able to accommodate all participants at the time that was arranged by the participant’s coordinators, institution administrators, and subject facilitators of the University of Dodoma, as described in Table  1 [ 7 ].

Evaluation of intervention

During the classroom presentation, there were 5 to 10 min for classroom discussion and reflection on the content presented, which was guided by the research assistant. During this time, the participants were given the opportunity to ask the questions, get clarification from the presenter, and provide their opinion on how the instructional messages were presented, content coverage, areas of strength and weakness for improvement, and academic growth. After the completion of the presentation sessions, the research assistant provided the questionnaire to participants in order to determine their presentation skills during the preparation of their assignments and classroom presentations using the Pecha Kucha presentation format.

Data analysis

The findings from this study were analyzed using the Statistical Package for Social Science (SPSS) computer software program version 26. The percentages, frequencies, frequency distributions, means, standard deviations, skewness, and kurtosis were calculated, and the results were presented using the figures, tables, and graphs. The mean score analysis was computed, and descriptive statistical analysis was used to analyze the demographic information of the participants in an attempt to determine the frequencies, percentages, and mean scores of their distributions. A paired sample t-test was used to compare the mean score differences of the presentation skills within the groups before and after the intervention. The mean score differences were determined based on the baseline scores against the post-intervention scores in order to establish any change in terms of presentation skills among the study participants.

The association between the Pecha Kucha presentation and the development of participants’ presentation skills was established using linear regression analysis set at a 95% confidence interval and 5% (≤ 0.05) significance level in an attempt to accept or reject the null hypothesis.

However, N-1 dummy variables were formed for the categorical independent variables so as to run the linear regression for the factors associated with the presentation skills. The linear regression equation with dummy variables is presented as follows:

Β 0 is the intercept.

Β 1 , Β 2 , …. Β k-1 are the coefficients which correspond to the dummy variables representing the levels of X 1 .

Β k is the coefficient which corresponds to the dummy variable representing the levels of X 2 .

Β k+1 is the coefficient which corresponds to the continuous predictor X 3 .

X 1,1 , X 1,2 ,……. X 1,k-1 are the dummy variables corresponding to the different levels of X 1 .

ε represents the error term.

The coefficients B1, B2… Bk indicate the change in the expected value of Y for each category relative to the reference category. If the Beta estimate is positive for the categorical or dummy variables, it means that the corresponding covariate has a positive impact on the outcome variable compared to reference category. However, if the beta estimate is positive for the case of continuous covariates, it means that the corresponding covariate has direct proportion effect on the outcome variables.

The distribution of the outcome variables was approximately normally distributed since the normality of the data is one of the requirements for parametric analysis. A paired t test was performed to compare the presentation skills of nursing students before and after the intervention.

Social-demographic characteristics of the study participants

The study involved a total of 230 nursing students, of whom 151 (65.65%) were male and the rest were female. The mean age of study participants was 23.03 ± 2.69, with the minimum age being 19 and the maximum age being 37. The total of 163 (70.87%) students, which comprised a large proportion of respondents, were aged less than or equal to 23, 215 (93.48%) participants were living on campus, and 216 (93.91) participants were exposed to social media.

A large number of study participants (82.17%) were pursuing a bachelor of Science in Nursing, with the majority being first-year students (30.87%). The total of 213 (92.61%) study participants had Form Six education as their entry qualification, with 176 (76.52%) participants being the product of public secondary schools and interested in the nursing profession. Lastly, the total of 121 (52.61%) study participants had never been exposed to any presentation training; 215 (93.48%) students had access to individual classroom presentations; and 227 (98.70%) study participants had access to group presentations during their learning process. The detailed findings for the participants’ social demographic information are indicated in Table  2 [ 46 ].

Baseline nursing students’ presentation skills using traditional powerPoint presentations

The current study assessed the participant’s presentation skills when preparing and presenting the materials before the audience using traditional PowerPoint presentations. The study revealed that the overall mean score of the participants’ presentation skills was 4.07 ± 0.56, including a mean score of 3.98 ± 0.62 for the participants’ presentation skills during the preparation of presentation content before the classroom presentation and a mean score of 4.18 ± 0.78 for the participants’ mastery of learning content before the classroom presentation. Moreover, the study revealed a mean score of 4.07 ± 0.71 for participants’ ability to prepare presentation materials for classroom presentations and a mean score of 4.04 ± 0.76 for participants’ ability to share the presentation materials in the classroom, as indicated in Table  3 [ 46 ].

Factors Associated with participants’ presentation skills through traditional powerPoint presentation

The current study revealed that the participants’ study program has a significant effect on their presentation skills, whereby being the bachelor of science in nursing was associated with a 0.37561 (P value < 0.027) increase in the participants’ presentation skills.The year of study also had significant effects on the participants’ presentation skills, whereby being a second-year bachelor student was associated with a 0.34771 (P value < 0.0022) increase in the participants’ presentation skills compared to first-year bachelor students and diploma students. Depending on loans as a source of student income retards presentation skills by 0.24663 (P value < 0.0272) compared to those who do not depend on loans as the source of income. Furthermore, exposure to individual presentations has significant effects on the participants’ presentation skills, whereby obtaining an opportunity for individual presentations was associated with a 0.33732 (P value 0.0272) increase in presentation skills through traditional PowerPoint presentations as shown in Table  4 [ 46 ].

Nursing student presentation skills through pecha kucha presentations

The current study assessed the participant’s presentation skills when preparing and presenting the materials before the audience using Pecha Kucha presentations. The study revealed that the overall mean score and standard deviation of participants’ presentation skills using the Pecha Kucha presentation format were 4.54 ± 0.59, including a mean score of 4.49 ± 0.66 for participant’s presentation skills during preparation of the content before classroom presentation and a mean score of 4.58 ± 0.65 for participants’ mastery of learning content before classroom presentation. Moreover, the study revealed a mean score of 4.58 ± 0.67 for participants ability to prepare the presentation materials for classroom presentation and a mean score of 4.51 ± 0.72 for participants ability to share the presentation materials in the classroom using Pecha Kucha presentation format as indicated in Table  5 [ 46 ].

Comparing Mean scores of participants’ presentation skills between traditional PowerPoint presentation and pecha kucha Presentation

The current study computed a paired t-test to compare and determine the mean change, effect size, and significance associated with the participants’ presentation skills when using the traditional PowerPoint presentation and Pecha Kucha presentation formats. The study revealed that the mean score of the participants’ presentation skills through the Pecha Kucha presentation was 4.54 ± 0.59 (p value < 0.0001) compared to the mean score of 4.07 ± 0.56 for the participants’ presentation skills using the traditional power point presentation with an effect change of 0.78. With regard to the presentation skills during the preparation of presentation content before the classroom presentation, the mean score was 4.49 ± 0.66 using the Pecha Kucha presentation compared to the mean score of 3.98 ± 0.62 for the traditional PowerPoint presentation. Its mean change was 0.51 ± 0.84 ( p  < .0001) with an effect size of 0.61.

Regarding the participants’ mastery of learning content before the classroom presentation, the mean score was 4.58 ± 0.65 when using the Pecha Kucha presentation format, compared to the mean score of 4.18 ± 0.78 when using the traditional power point presentation. Its mean change was 0.40 ± 0.27 ( p  < .0001) with an effect size of 1.48. Regarding the ability of the participants to prepare the presentation materials for classroom presentations, the mean score was 4.58 ± 0.67 when using the Pecha Kucha presentation format, compared to 4.07 ± 0.71 when using the traditional PowerPoint presentation. Its mean change was 0.51 ± 0.96 ( p  < .0001) with an effect size of 0.53.

Regarding the participants’ presentation skills when sharing the presentation material in the classroom, the mean score was 4.51 ± 0.72 when using the Pecha Kucha presentation format, compared to 4.04 ± 0.76 when using the traditional PowerPoint presentations. Its mean change was 0.47 ± 0.10, with a large effect size of 4.7. Therefore, Pecha Kucha presentation pedagogy has a significant effect on the participants’ presentation skills than the traditional PowerPoint presentation as shown in Table  6 [ 46 ].

Factors associated with presentation skills among nursing students through pecha kucha presentation

The current study revealed that the participant’s presentation skills using the Pecha Kucha presentation format were significantly associated with knowledge of the Pecha Kucha presentation format, whereby increase in knowledge was associated with a 0.0239 ( p  < .0001) increase in presentation skills. Moreover, the current study revealed that the presentation through the Pecha Kucha presentation format was not influenced by the year of study, whereby being a second-year student could retard the presentation skills by 0.23093 (p 0.039) compared to a traditional PowerPoint presentation. Other factors are shown in Table  7 [ 46 ].

Social-demographic characteristics profiles of participants

The proportion of male participants was larger than the proportion of female participants in the current study. This was attributable to the distribution of sex across the nursing students at the university understudy, whose number of male nursing students enrolled was higher than female students. This demonstrates the high rate of male nursing students’ enrolment in higher training institutions to pursue nursing and midwifery education programs. Different from the previous years, the nursing training institutions were predominantly comprised of female students and female nurses in different settings. This significant increase in male nursing students’ enrollment in nursing training institutions predicts a significant increase in the male nursing workforce in the future in different settings.

These findings on Pecha Kucha as an alternative to PowerPoint presentations in Massachusetts, where the proportion of female participants was large as compared to male participants, are different from the experimental study among English language students [ 29 ]. The referred findings are different from the results of the randomized control study among the nursing students in Anakara, Turkey, where a large proportion of participants were female nursing students [ 47 ]. This difference in participants’ sex may be associated with the difference in socio-cultural beliefs of the study settings, country’s socio-economic status, which influence the participants to join the nursing profession on the basis of securing employment easily, an opportunity abroad, or pressure from peers and parents. Nevertheless, such differences account for the decreased stereotypes towards male nurses in the community and the better performance of male students in science subjects compared to female students in the country.

The mean age of the study participants was predominantly young adults with advanced secondary education. Their ages reflect adherence to national education policy by considering the appropriate age of enrollment of the pupils in primary and secondary schools, which comprise the industries for students at higher training institutions. This age range of the participants in the current study suits the cognitive capability expected from the participants in order to demonstrate different survival and life skills by being able to set learning goals and develop strategies to achieve their goals according to Jean Piaget’s theory of cognitive learning [ 41 , 42 ].

Similar age groups were noted in the study among nursing students in a randomized control study in Anakara Turkey where the average age was 19.05 ± 0.2 [ 47 ]. A similar age group was also found in a randomized control study among liberal arts students in Anakara, Turkey, on differences in instructor, presenter, and audience ratings of Pecha Kucha presentations and traditional student presentations where the ages of the participants ranged between 19 and 22 years [ 49 ].

Lastly, a large proportion of the study participants had the opportunity for individual and group presentations in the classroom despite having not been exposed to any presentation training before. This implies that the teaching and learning process in a nursing education program is participatory and student-centered, thus giving the students the opportunity to interact with learning contents, peers, experts, webpages, and other learning resources to become knowledgeable. These findings fit with the principle that guides and facilitates the student’s learning from peers and teachers according to the constructivism theory of learning by Lev Vygotsky [ 48 ].

Effects of pecha kucha presentation pedagogy on participants’ presentation skills

The participants’ presentation skills were higher for Pecha Kucha presentations compared with traditional PowerPoint presentations. This display of the Pecha Kucha presentation style enables the nursing students to prepare the learning content, master their learning content before classroom presentations, create good presentation materials and present the materials, before the audience in the classroom. This finding was similar to that at Padang State University, Indonesia, among first-year English and literature students whereby the Pecha Kucha Presentation format helped the students improve their skills in presentation [ 20 ]. Pecha Kucha was also found to facilitate careful selection of the topic, organization and outlining of the students’ ideas, selection of appropriate images, preparation of presentations, rehearsing, and delivery of the presentations before the audience in a qualitative study among English language students at the Private University of Manila, Philippines [ 23 ].

The current study found that Pecha Kucha presentations enable the students to perform literature searches from different webpages, journals, and books in an attempt to identify specific contents during the preparation of the classroom presentations more than traditional PowerPoint presentations. This is triggered by the ability of the presentation format to force the students to filter relevant and specific information to be included in the presentation and search for appropriate images, pictures, or figures to be presented before the audience. Pecha Kucha presentations were found to increase the ability to perform literature searches before classroom presentations compared to traditional PowerPoint presentations in an experimental study among English language students at Worcester State University [ 29 ].

The current study revealed that Pecha Kucha presentations enable the students to create a well-structured classroom presentation effectively by designing 20 meaningful and content-rich slides containing 20 images, pictures, or figures and a transitional flow of 20 s for each slide, more than the traditional PowerPoint presentation with an unlimited number of slides containing bullets with many texts or words. Similarly, in a cross-sectional study of medical students in India, Pecha Kucha presentations were found to help undergraduate first-year medical students learn how to organize knowledge in a sequential fashion [ 26 ].

The current study revealed that Pecha Kucha presentations enhance sound mastery of the learning contents and presentation materials before the classroom presentation compared with traditional PowerPoint presentations. This is hastened by the fact that there is no slide reading during the classroom Pecha Kucha presentation, thus forcing students to read several times, rehearse, and practice harder the presentation contents and materials before the classroom presentation. Pecha Kucha presentation needed first year English and literature students to practice a lot before their classroom presentation in a descriptive qualitative study at Padang State University-Indonesia [ 20 ].

The current study revealed that the participants became more confident in answering the questions about the topic during the classroom presentation using the Pecha Kucha presentation style than during the classroom presentation using the tradition PowerPoint presentation. This is precipitated by the mastery level of the presentation contents and materials through rehearsal, re-reading, and material synthesis before the classroom presentations. Moreover, Pecha Kucha was found to significantly increase the students’ confidence during classroom presentation and preparation in a qualitative study among English language students at the Private University of Manila, Philippines [ 23 ].

Hence, there was enough evidence to reject the null hypothesis in that there was no significant difference in nursing students’ presentation skills between the baseline and end line. The Pecha Kucha presentation format has a significant effect on nursing student’s classroom presentation skills as it enables them to prepare the learning content, have good mastery of the learning contents, create presentation materials, and confidently share their learning with the audience in the classroom.

The current study’s findings complement the available pieces of evidence on the effects of Pecha Kucha presentations on the students’ learning and development of survival life skills in the 21st century. Pecha kucha presentations have more significant effects on the students’ presentation skills compared with traditional PowerPoint presentations. It enables the students to select the topic carefully, organize and outline the presentation ideas, select appropriate images, create presentations, rehearse the presentations, and deliver them confidently before an audience. It also enables the students to select and organize the learning contents for classroom presentations more than traditional PowerPoint presentations.

Pecha Kucha presentations enhance the mastery of learning content by encouraging the students to read the content several times, rehearse, and practice hard before the actual classroom presentation. It increases the students’ ability to perform literature searches before the classroom presentation compared to a traditional PowerPoint presentation. Pecha Kucha presentations enable the students to create well-structured classroom presentations more effectively compared to traditional PowerPoint presentations. Furthermore, Pecha Kucha presentations make the students confident during the presentation of their assignments and project works before the audience and during answering the questions.

Lastly, Pecha Kucha presentations enhance creativity among the students by providing the opportunity for them to decide on the learning content to be presented. Specifically, they are able to select the learning content, appropriate images, pictures, or figures, organize and structure the presentation slides into a meaningful and transitional flow of ideas, rehearse and practice individually before the actual classroom presentation.

Strength of the study

This study has addressed the pedagogical gap in nursing training and education by providing new insights on the innovative students’ presentation format that engages students actively in their learning to bring about meaningful and effective students’ learning. It has also managed to recruit, asses, and provide intended intervention to 230 nursing students without dropout.

Study limitation

The current study has pointed out some of the strengths of the PechaKucha presentations on the students’ presentation skills over the traditional students’ presentations. However, the study had the following limitations: It involved one group of nursing students from one of the public training institutions in Tanzania. The use of one university may obscure the interpretation of the effects of the size of the intervention on the outcome variables of interest, thus limiting the generalization of the study findings to all training institutions in Tanzania. Therefore, the findings from this study need to be interpreted by considering this limitation. The use of one group of nursing students from one university to explore their learning experience through different presentation formats may also limit the generalization of the study findings to all nursing students in the country. The limited generalization may be attributed to differences in socio-demographic characteristics, learning environments, and teaching and learning approaches. Therefore, the findings from this study need to be interpreted by considering this limitation.

Suggestions for future research

The future research should try to overcome the current study limitations and shortcomings and extend the areas assessed by the study to different study settings and different characteristics of nursing students in Tanzania as follows: To test rigorously the effects of Pecha Kucha presentations in enhancing the nursing students’ learning, the future studies should involve nursing students’ different health training institutions rather than one training institution. Future studies should better use the control students by randomly allocating the nursing students or training institutions in the intervention group or control group in order to assess the students’ learning experiences through the use of Pecha Kucha presentations and PowerPoint presentations consecutively. Lastly, future studies should focus on nursing students’ mastery of content knowledge and students’ classroom performance through the use of the Pecha Kucha presentation format in the teaching and learning process.

Data availability

The datasets generated and analyzed by this study can be obtained from the corresponding author on reasonable request through [email protected] & [email protected].

Abbreviations

Doctor (PhD)

Multimedia Theory of Cognitive Learning

National Council for Technical and Vocational Education and Training

Principle Investigator

Pecha Kucha presentation

Statistical Package for Social Sciences

Tanzania Commission for Universities

World Health Organization

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Acknowledgements

The supervisors at the University of Dodoma, statisticians, my employer, family members, research assistants and postgraduate colleagues are acknowledged for their support in an attempt to facilitate the development and completion of this manuscript.

The source of funds to conduct this study was the registrar, Tanzania Nursing and Midwifery Council (TNMC) who is the employer of the corresponding author. The funds helped the author in developing the protocol, printing the questionnaires, and facilitating communication during the data collection and data analysis and manuscript preparation.

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Setberth Jonas Haramba & Walter C. Millanzi

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S.J.H: conceptualization, proposal development, data collection, data entry, data cleaning and analysis, writing the original draft of the manuscript W.C.M: Conceptualization, supervision, review, and editing of the proposal, and the final manuscript S.S.A: Conceptualization, supervision, review, and editing of the proposal and the final manuscript.

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Correspondence to Setberth Jonas Haramba .

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All methods were carried out under the relevant guidelines and regulations. Since the study involved the manipulation of human behaviors and practices and the exploration of human internal learning experiences, there was a pressing need to obtain ethical clearance and permission from the University of Dodoma (UDOM) Institution of Research Review Ethics Committee (IRREC) in order to conduct this study. The written informed consents were obtained from all the participants, after explaining to them the purpose, the importance of participating in the study, the significance of the study findings to students’ learning, and confidentiality and privacy of the information that will be provided. The nursing students who participated in this study benefited from the knowledge of the Pecha Kucha presentation format and how to prepare and present their assignments using the Pecha Kucha presentation format.

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Haramba, S.J., Millanzi, W.C. & Seif, S.A. Effects of pecha kucha presentation pedagogy on nursing students’ presentation skills: a quasi-experimental study in Tanzania. BMC Med Educ 24 , 952 (2024). https://doi.org/10.1186/s12909-024-05920-2

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Received : 16 October 2023

Accepted : 16 August 2024

Published : 31 August 2024

DOI : https://doi.org/10.1186/s12909-024-05920-2

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