Aplicaciones interactivas basadas en el paquete SHINY/R para explicar conceptos estadísticos

Un mapeo sistemático de la literatura

Autores/as

  • Álvaro Toledo San Martín Universidad Bernardo O’Higgins
  • Inés Vicencio Pardo Universidad Bernardo O’Higgins

DOI:

https://doi.org/10.37467/revhuman.v12.4740

Palabras clave:

TIC, Enseñanza de la estadística, Aplicaciones interactivas, Shiny/R, Mapeo sistemático

Resumen

Shiny es una aplicación para el software R que permite la creación de interfaces para usuarios sin conocimiento de programación. En este trabajo utilizamos em método de mapeo sistemático para la recopilación, análisis y extracción de información en publicaciones que indican el uso de Shiny para explicar conceptos estadísticos. Dentro de las conclusiones se tiene que Shiny es utilizado como herramienta para la realización de experiencias académicas, además como medio para la solución de problemas en las áreas de educación y ciencias naturales y de la vida abordando tópicos de estadística relacionados con estadística pre-inferencial e inferencial, entre otros.

Citas

Arnholt, A. T. (2019). Using a Shiny app to teach the concept of power. Teaching Statistics, 41(3), 79-84.

Baumer, B., Cetinkaya-Rundel, M., Bray, A., Loi, L., & Horton, N. J. (2014). R Markdown: Integrating a reproducible analysis tool into introductory statistics. arXiv preprint arXiv:1402.1894.

Beeley, C. (2016). Web application development with R using Shiny. Packt Publishing Ltd.

Berg, A. (2021). Bayesian explorations with dice. Teaching Statistics, 43(3), 114-123.

Boza Carreño, Á., Tirado Morueta, R., y Guzmán Franco, M. D. (2010). Creencias del profesorado sobre el significado de la tecnología en la enseñanza: influencia para su inserción en los centros docentes andaluces. Revista electrónica de investigación y Evaluación educative.

Chance, B. & Rossman, A. (2006). Using Simulation to Teach and Learn Statistics. In Proceedings of the Seventh International Conference on Teaching Statistics (pp. 1-6). Voorburg, The Netherlands: International Statistical Institute.

Chang, W., Cheng, J., Allaire, JJ, Xie, Y. & McPherson, J. (2021). shiny: Web Application Framework for R. R package version 1.7.1.

Disponible en https://cran.r-project.org/web/packages/shiny/index.html

Das, K. (2019). Role of ICT for Better Mathematics Teaching. Shanlax International Journal of Education, 7(4), 19-28.

Depaoli, S., Winter, S. D., & Visser, M. (2020). The importance of prior sensitivity analysis in Bayesian statistics: demonstrations using an interactive Shiny App. Frontiers in psychology, 11, 608045.

Di Iorio, J., & Vantini, S. (2021). How to Get Away with Statistics: Gamification of Multivariate Statistics. Journal of Statistics and Data Science Education, 29(3), 241-250.

Fernández, Z. y Neri, C. (2013). Estudiantes universitarios, TICS y aprendizaje. Anuario de Investigaciones, 20(3), 153-158.

Fawcett, L. (2018). Using interactive shiny applications to facilitate research-informed learning and teaching. Journal of Statistics Education, 26(1), 2-16.

Garfield, J. & Ben-Zvi, D. (2008). Developing Students’ Statistical Reasoning: Connecting Research and Teaching Practice, Kluwer Academic Publishers.

Gopinath, P. P., Parsad, R., Joseph, B., & Adarsh, V. S. (2021). grapesAgri1: collection of shiny apps for data analysis in agriculture. Journal of Open Source Software, 6(63), 3437

Harraway (2012). Learning Statistics Using Motivational Videos, Real Data and Free Soft- ware, Technology Innovations in Statistics Education, 6(1). https://doi.org/10.5070/T561000186

Johnson O, Fronterre C, Diggle PJ, Amoah B, Giorgi E (2021) MBGapp: A Shiny application for teaching model-based geostatistics to population health scientists. PLoS ONE 16(12): e0262145. https://doi.org/10.1371/journal.pone.0262145

LaMar, M., & Donovan, S. (2017). Building a gateway between classrooms and data science using QUBESHub. Gateways, 23-25.

Mairing, J. P. (2020). The Effect of Advance Statistics Learning Integrated Minitab and Excel with Teaching Teams. International Journal of Instruction, 13(2), 139-150.

Manjunath, M., Zhang, Y., Yeo, S. H., Sobh, O., Russell, N., Followell, C., ... & Song, J. S. (2017). ClusterEnG: An interactive educational web resource for clustering big data. bioRxiv, 120915.

Miranda Freire, S. (2019). Using Shiny to illustrate the probability density function concept. Teaching Statistics, 41(1), 30-35.

Mochón, J. F. C., Carrillo, J. R., Jiménez, E. S., y López, A. M. (2021). Didáctica de las matemáticas, software libre y desarrollo de recursos mediante Learnr y Shiny. EDUCATECONCIENCIA, 29(31), 101-121.

Moore, T. N., Thomas, R. Q., Woelmer, W. M., & Carey, C. C. (2022). Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module. Forecasting, 4(3), 604-633.

Murillo, D. A., Gezan, S. A., Heilman, A. M., Walk, T. C., Aparicio, J. S., & Horsley, R. D. (2021). FielDHub: A shiny app for design of experiments in life sciences. The Journal of Open Source Software.

Nolan, D. & Speed, T. P. (2000). Stat Labs: Mathematical Statistics Through Applications, Springer. http://www.stat.Berkeley.edu/users/statlabs/

Panchenko, L. & Khomiak, A. (2020). Education Statistics: Looking for а Case-study for Modelling.

Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008, June). Systematic mapping studies in software engineering. In 12th International Conference on Evaluation and Assessment in Software Engineering (EASE) 12 (pp. 1-10).

Pfleeger, S. L. (2005). Soup or art? The role of evidential force in empirical software engineering. IEEE software, 22(1), 66-73.

Potter, G., Wong, J., Alcaraz, I., & Chi, P. (2016). Web application teaching tools for statistics using R and shiny. Technology Innovations in Statistics Education, 9(1).

R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Satyahadewi, N., & Perdana, H. (2021, May). Web Application Development for Inferential Statistics using R Shiny. In 1st International Conference on Mathematics and Mathematics Education (ICMMEd 2020) (pp. 425-429). Atlantis Press.

Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. CRC Press.

Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte Carlo simulation. Journal of Statistics Education, 24(3), 136-156.

Stratton, C., Green, J. L., & Hoegh, A. (2021). Not just normal: Exploring power with Shiny apps. Technology Innovations in Statistics Education, 13(1).

Toledo, Á. y Vicencio, I. (2021). El uso de TIC para el aprendizaje de la Estadística: un estudio en universidades chilenas. Educação e ensino na era da informação, 40-58.

Varma, J. R., & Virmani, V. (2017). Shiny alternative for Finance in the Classroom.

Von Borries, G. F., & De Castro Quadros, A. VV. (2022). ROC App: an application to understand roc curves. Brazilian Journal of Biometrics, 40(2).

Wang, S. L., Zhang, A. Y., Messer, S., Wiesner, A., & Pearl, D. K. (2021). Student-Developed Shiny Applications for Teaching Statistics. Journal of Statistics and Data Science Education, 29(3), 218-227.

Wen, J., Li, S., Lin, Z., Hu, Y., & Huang, C. (2012). Systematic literature review of machine learning based software development effort estimation models. Information and Software Technology, 54(1), 41-59.

Wickham, H. (2011). ggplot2. Wiley Interdisciplinary Reviews: Computational Statistics, 3(2), 180-185. https://doi.org/10.1002/wics.147

Wickham, H. (2021). Mastering shiny. “ O’Reilly Media, Inc.”.

Williams, I. J., & Williams, K. K. (2018). Using an R shiny to enhance the learning experience of confidence intervals. Teaching Statistics, 40(1), 24-28.

Wojciechowski, J., Hopkins, A. M., & Upton, R. N. (2015). Interactive pharmacometric applications using R and the shiny package. CPT: pharmacometrics & systems pharmacology, 4(3), 146-159.

Zieffler, A., Park, J., Gareld, J., delMas, R. & Bjornsdottir, A. (2012). The Statistics. Teaching Inventory: A Survey on Statistics Teachers’ Classroom Practices and Beliefs, Journal of Statistics Education, 20(1). https://doi.org/10.1080/10691898.2012.11889632

Descargas

Publicado

2023-02-14

Cómo citar

Toledo San Martín, Álvaro, & Vicencio Pardo, I. (2023). Aplicaciones interactivas basadas en el paquete SHINY/R para explicar conceptos estadísticos: Un mapeo sistemático de la literatura. HUMAN REVIEW. International Humanities Review Revista Internacional De Humanidades, 17(4), 1–15. https://doi.org/10.37467/revhuman.v12.4740

Número

Sección

Artículos de investigación (monográfico)