The teaching-learning process that requires the manipulation or visualization of objects has some limitations. Augmented reality and mobile devices applied to videogames make it possible to solve these deficiencies by offering the possibility of interacting with virtual objects in a three-dimensional space. The objective of this article is to analyze the effects and motivation of gamification and augmented reality using Voluminis, in sixth-grade students of the mathematics subject, in a public school. This paper’s methodology are as follows: architecture of Voluminis, design and implementation of the geometry game prototype, and this was to applied is the cuasi-experimental design with 21 Peruvian 6th-grade school children. The results show that the proposed learning scheme improves learning motivation and the teaching of spatial geometry.
|Title of host publication||Human-Computer Interaction - 6th Iberomarican Workshop, HCI-Collab 2020, Proceedings|
|Editors||Vanessa Agredo-Delgado, Pablo H. Ruiz, Vanessa Agredo-Delgado, Pablo H. Ruiz, Klinge Orlando Villalba-Condori|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||10|
|State||Published - 2020|
|Event||6th Ibero-American Conference on Human-Computer Interaction, HCI-Collab 2020 - Arequipa, Peru|
Duration: 16 Sep 2020 → 18 Sep 2020
|Name||Communications in Computer and Information Science|
|Conference||6th Ibero-American Conference on Human-Computer Interaction, HCI-Collab 2020|
|Period||16/09/20 → 18/09/20|
Bibliographical noteFunding Information:
Thanks to the ?Research Center, Transfer of Technologies and Software Development R + D + i?-CiTeSoft EC-0003-2017-UNSA, for their collaboration in the use their equipment and facilities, and the Instituci?n Educativa Se?or de Huanca CIRCA (Arequipa-Per?) for the development of this research work, and Prof. Juan Carlos Pulcha-Colegio De Los Sagrados Corazones (Arequipa-Per?).
© 2020, Springer Nature Switzerland AG.
- Mathematics learning
- Mobile augmented reality
- Solid geometry
- System learning