Machine Learning as a Methodological Resource in the Classroom

We currently find ourselves in a society dependent on the electronic devices that surround us. Many of these machines work through programming, robotics and Artificial Intelligence (AI) in daily tasks and every day we consciously or unconsciously use it, such as the use of smartphones, computers, cars, kitchen robots, the use of credit cards. Sometimes these devices in our personal lives, cities or industries are connected to the Internet and are called the Internet of Things (IoT). For a device to respond to human actions, it needs a training called “Machine Learning”. Machine Learning is a branch of artificial intelligence that focuses on developing algorithms and models that allow machines to learn and make decisions based on data and previous experience, rather than being explicitly programmed. This process can be transferred to the classroom as a methodological and transversal resource in different educational stages from early childhood education, promoting computational thinking of students. In this chapter we present the operation of LearningML with some practical educational examples. The objective of the study is to analyze the impact of the LearningML machine learning resource in a learning situation in sixth grade students of Primary Education. Specifically, the results of the learning situation related to the Sustainable Development Goal (SDG) 11, sustainable cities and communities, and the impact among students will be analyzed. It can be concluded that AI has become an important part of our daily lives and schools should teach students about the knowledge and ethical use of these technologies. ML enables students to acquire basic problem solving and computational thinking skills. There are open online educational programmes and resources that allow interdisciplinary projects to be carried out and students to develop their digital competence from Pre-school, Primary, Secondary and Post-secondary. AI offers very interesting educational possibilities, as it allows students to learn about how machine learning algorithms work and how they can be applied in real life. In addition, multidisciplinary and interdisciplinary content can be worked on and the relevance of learning with the available technological tools and devices is highlighted in order to train critical students who are prepared to meet the needs of the labour market.