Digital Learning Tools for Mobile Devices for Accomplish Hypothesis Testing of Statistical Parameters
Dublin Core
Title
Digital Learning Tools for Mobile Devices for Accomplish Hypothesis Testing of Statistical Parameters
Subject
digital learning tools
u-learning
m-learning
hypothesis test
statistics.
Description
Technological changes have been associated with the evolution of computer and telecommunications systems. These changes have resulted in a rethinking of teaching and learning methods in the new digitalized environment at all educational levels. This rethinking motivates some teachers to design new digital tools that support students in their learning process, offering them an easier and more entertaining way to obtain knowledge. The digital learning tools are software and informatics programs that make everyday activities easier for students. We have designed four digital learning tools for the learning of inferential statistics that allow college students to perform hypothesis tests for: i) the arithmetic mean of the population, ii) the proportion of a population, iii) the difference between two arithmetic means, and iv) the difference between two proportions. These digital learning tools are products from the project “Statistics-to-Go” that is being developed at the University of Sonora. This project is now in its fourth stage.
Creator
Tapia Moreno, Francisco Javier
Villa-Martinez, Hector Antonio
Source
International Journal of Interactive Mobile Technologies (iJIM); Vol. 13 No. 06 (2019); pp. 15-26
1865-7923
Publisher
International Association of Online Engineering (IAOE), Vienna, Austria
Date
2019-06-20
Rights
Copyright (c) 2019 Francisco Javier Tapia Moreno, Hector Antonio Villa-Martinez
Relation
Format
application/pdf
Language
eng
Type
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Identifier
Citation
Tapia Moreno, Francisco Javier and Villa-Martinez, Hector Antonio, Digital Learning Tools for Mobile Devices for Accomplish Hypothesis Testing of Statistical Parameters, International Association of Online Engineering (IAOE), Vienna, Austria, 2019, accessed November 8, 2024, https://igi.indrastra.com/items/show/1442