Overview of the problem

• Although universities purportedly value the nexus between teaching and research, coursework students do not often contribute significantly to the research activity of universities—besides their role as participants of many studies. 

• Consequently, many coursework students do not develop adequate skills in research methods—such as data science—skills that are likely to become more coveted in the future as AI evolves

• Instead, the contribution of coursework students to the research activity of unis could diminish the costs, and enhance the productivity, of research.

 

Overview of a solution

• Universities should launch a platform that encourages, assists, and rewards teaching staff who design assignments and exercises that contribute towards the research activity of the institution.

• For example, to help teaching staff design these assignments, universities could invest in algorithms that match the research needs of researchers with the learning needs of students.

• These algorithms, such as latent semantic analysis, would analyze research plans, research proposals, course handbooks, and similar documents.  The analyses would uncover activities that might benefit the needs of researchers and students concurrently.  

• Teaching staff could then design assignments and exercises that include these activities.

 

Examples of this overlap 

• In one assignment, students might be instructed to construct Wikipedia entries that promote the research of their university

• The teaching staff who designed this assignment might benefit if the researchers agree to grade the assignments.

• Furthermore, algorithm could prioritize the research interests of these teaching staff—increasing the likelihood that a student will complete an assignment that benefits their own research.