Nowadays, Augmented Reality (AR) is one of the emerging technologies with a greater impact in the Education field. Research has proved that AR-based activities improve the teaching and learning processes. Also, the use of this type of technology in classroom facilitates the understanding of contents from different areas as Arts, Mathematics or Science. In this work we propose an AR-based instruction in order to explore the benefits in a 6th-grade Primary course related to 3D-geometry shapes. This first experiment, designed from an exploratory approach, will shed light on new study variables to perform new implementations whose conclusions become more consistent. The results obtained allow us to envisage that AR-based proposals slightly improve the classical didactic methods.

In this work, a model for the simulation of infectious disease outbreaks including mobility data is presented. The model is based on the SAIR compartmental model and includes mobility data terms that model the flow of people between different regions. The aim of the model is to analyze the influence of mobility on the evolution of a disease after a lockdown period and to study the appearance of small epidemic outbreaks due to the so-called imported cases. We apply the model to the simulation of the COVID-19 in the various areas of Spain, for which the authorities made available mobility data based on the position of cell phones. We also introduce a method for the estimation of incomplete mobility data. Some numerical experiments show the importance of data completion and indicate that the model is able to qualitatively simulate the spread tendencies of small outbreaks. This work was motivated by an open call made to the mathematical community in Spain to help predict the spread of the epidemic.

Educational robotics are commonly present in kindergarten and primary school classrooms, particularly Bee-bot. Its ease of use allows the introduction of computer programming to young children in educational contexts from a science, technology, engineering, arts, and mathematics (STEAM) perspective. Despite this rise, there are still few investigations that collect evidence on the effectiveness of robotic interventions. Although mentoring experiences with robotics had been carried out in educational contexts, this work explores their effect on the acquisition of computational thinking skills through mentoring. Participants from the second grade, aged seven through eight years, were exposed to two sessions of robotics with Bee-bot in order to promote hands-on experimentation. The sessions were conducted by nine students of the fourth grade (the mentors), aged 10 to 11 years. A descriptive case-study methodology was employed for the analysis of the mentoring intervention. The effect of the mentoring experience was assessed in terms of motivation and computational thinking skills. Mixed quantitative and qualitative results show two important findings: (i) Mentoring is a powerful tool to be considered for improvement of the motivation and cooperation of students in their teaching–learning process, and (ii) computational thinking skills can be acquired by second-grade students through a mentoring process.