Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review - Orji, Fidelia A.; Vassileva, Julita (Frontiers in Artificial Intelligence, 2022)
Student characteristics affect their willingness and ability to acquire new knowledge. Assessing and identifying the effects of student characteristics is important for online educational systems. Machine learning (ML) is becoming significant in utilizing learning data for student modelling, decision support systems, adaptive systems, and evaluation systems. The growing need for dynamic assessment of the student characteristics in online educational systems has led to application of machine learning methods in modelling the characteristics. Being able to automatically model student characteristics during learning processes is essential for dynamic and continuous adaptation of teaching and learning to each student's needs. This paper provides a review of 8 years (from 2015 to 2022) of literature on the application of machine learning methods for automatic modelling of various student characteristics to 1) summarize and highlight progress and trends in research to date, 2) identify modelled student characteristics and machine learning techniques used, and 3) suggest directions for future research.