ANALYSIS OF THE HYBRID ADAPTIVE LEARNING MODEL IN HIGHER EDUCATION

• DOI: 10.22533/at.ed.5583382310109

• Palavras-chave: adaptive learning, higher education, educational technologies, personalization of learning, machine learning.

• Keywords: adaptive learning, higher education, educational technologies, personalization of learning, machine learning.

• Abstract:

  Hybrid adaptive learning in higher education is a strategy that combines in-person and online teaching to create personalized learning experiences for each student. Within this strategy, it is important to consider the different learning styles of students, such as visual, auditory and kinesthetic styles, to achieve effective adaptation of teaching.
  Literature has demonstrated the effectiveness of using technology in hybrid adaptive learning and its ability to adapt to different student learning styles. Teachers also play an important role in hybrid adaptive learning, as they must monitor and guide students' progress online and in the classroom.
  The use of a learning platform that includes elements of artificial intelligence and machine learning can be a useful tool in adapting learning to the different learning styles of students. However, it is important that decision-making regarding adaptation of learning is a collaborative process that involves both teachers and students.
  In this context, hybrid adaptive learning is an effective strategy to create personalized learning experiences that consider the different learning styles of students. The use of technology, collaboration between teachers and students, and data-driven decision making can significantly improve student learning.