Use this identifier to quote or link this document: http://hdl.handle.net/2072/449921

Systematic Review on Which Analytics and Learning Methodologies Are Applied in Primary and Secondary Education in the Learning of Robotics Sensors
Amo Filvà, Daniel; Fox, Paul; Fonseca Escudero, David; Poyatos, César
Universitat Ramon Llull. La Salle; Universidad Autónoma de Madrid
Robotics technology has become increasingly common both for businesses and for private citizens. Primary and secondary schools, as a mirror of societal evolution, have increasingly integrated science, technology, engineering and math concepts into their curricula. Our research questions are: “In teaching robotics to primary and secondary school students, which pedagogical-methodological interventions result in better understanding and knowledge in the use of sensors in educational robotics?”, and “In teaching robotics to primary and secondary school students, which analytical methods related to Learning Analytics processes are proposed to analyze and reflect on students’ behavior in their learning of concepts and skills of sensors in educational robotics?”. To answer these questions, we have carried out a systematic review of the literature in the Web of Science and Scopus databases regarding robotics sensors in primary and secondary education, and Learning Analytics processes. We applied PRISMA methodology and reviewed a total of 24 articles. The results show a consensus about the use of the Learning by Doing and Project-Based Learning methodologies, including their different variations, as the most common methodology for achieving optimal engagement, motivation and performance in students’ learning. Finally, future lines of research are identified from this study.
2020-12
004 - Informàtica
373 - Ensenyament primari i secundari
62 - Enginyeria. Tecnologia
Robòtica
Ensenyament primari -- Innovacions tecnològiques
Ensenyament secundari -- Innovacions tecnològiques
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© L'autor/a
21 p.
Article
Article - Published version
https://doi.org/10.3390/s21010153
MDPI
Sensors, 2020, Vol. 21, No. 1
         

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