Analysis and Design of the Web Game on Descriptive Statistics through the ADDIE Model, Data Science and Machine Learning

Ricardo-Adán Salas-Rueda, Érika-Patricia Salas-Rueda, Rodrigo-David Salas-Rueda
4588 2642

Abstract


This mixed research aims to analysis and design the Web Game On Descriptive Statistics (WGODS) through the ADDIE model, data science and machine learning. The sample consists of 61 students from a university in Mexico. WGODS is a technological tool (quiz game) that presents various questions and answers about statistics (quantitative and qualitative data). The results of the linear regression (machine learning) indicate that the content and aesthetics of WGODS have a positive influence on the educational process. The ADDIE model allows the organization of WGODS considering the needs of the students. Also, data science identifies 4 predictive models on the use of WGODS in the field of statistics through the decision tree technique. Finally, teachers can transform the organization and development of school activities through the ADDIE model and technology. In particular, WGODS improves the educational process on the quantitative and qualitative data through a pleasant, attractive, simple, easy and useful web interface.

Keywords


Technology, ADDIE model, Learning, Data science

Full Text:

PDF

References


Salas-Rueda, R. A., Salas-Rueda, E. P., & Salas-Rueda, R. D. (2020). Analysis and design of the web game on descriptive statistics through the ADDIE model, data science and machine learning. International Journal of Education in Mathematics, Science and Technology (IJEMST), 8(3), 245-260.




DOI: https://doi.org/10.46328/ijemst.v8i3.759

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 International Journal of Education in Mathematics, Science and Technology

Web of Science (ESCI) Index                        

                                    

  

International Journal of Education in Mathematics, Science and Technology (IJEMST) 
 
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

 

ISSN: 2147-611X (Online)