An evaluation of machine learning approaches in educational environments: a systematic literature review

Abstract The goal of this article is to investigate how methodologies based on machine learning are being applied in educational environments to enhance the quality of learning in graduation courses. The method is presented through a systematic review of literature on the main methodologies of systems based on artificial intelligence applied in smart classrooms. The results indicate that in the popularization of machine learning, the widespread use of these technologies presents new challenges for educators as learning agents who investigate learning strategies alongside their learners. The learning with these recent technologies faces challenges regarding the veracity, soundness, and quality of information. In the classroom, it is important to take into account the legal aspects and ethics principles involved in these recent machine learning advances. Besides, these issues involve how popular platforms combine data from multiple sources to generate useful information with reduced human intervention. Nowadays, society lives connected to a myriad of interconnected computational devices. In this sense, the classrooms are evolving into learning spaces where students learn together in a collaborative way and shape soft-skills abilities, such as communication, relationship, and collaboration.