Understanding the role of AI and learning analytics techniques in addressing task difficulties in STEM education

This chapter provides a discussion of the notion of task difficulty, and outlines the key challenges inherent in defining and evaluating task difficulty in digital learning environments (DLEs). It highlights how the recent advances in learning analytics and artificial intelligence may help in assessing task difficulty in science, technology, engineering, and mathematics (STEM) as well as directions for future research. The role of students' perceptions in task difficulty has also been explored within medical education in the context of simulation-based practice and training. Despite global recognition of the growing importance of STEM education, current STEM education is strewn with many problems, including lack of diversity in graduates, low student enrolment and attrition rate, teachers' professional development, inappropriate curriculum, and misalignment between STEM curricula and rapidly changing market requirements. DLEs offer flexibility and convenience to students in terms of how and when they can access the course contents. Computer games are a great tool for learning in STEM.