Computers as teaching assistants: automated intelligent feedback in university statistics education

Introductory statistics courses are both essential and challenging for many university students. Students struggle to understand the abstract concepts involved, such as significance level and p-value, and the role of uncertainty in statistical procedures. Appropriate feedback could support students in gaining understanding, but is difficult to provide for teachers, since the number of students enrolled in such courses is often large. In my PhD project, a solution is sought in automated feedback in an Intelligent Tutoring System, guided by the question: How can automated intelligent feedback support first-year university students in developing statistical proficiency? In two first-year introductory statistics courses for social sciences students, two feedback types were implemented: inner loop feedback on steps in hypothesis-testing tasks by a domain reasoner and outer loop feedback over series of tasks in the form of inspectable student models.
In this talk, I will show how we have designed and implemented these two feedback types, how students have used them while doing their statistics homework tasks and how we think this has affected their learning. There will be plenty of time to discuss implications of this research project for university statistics education, as well as applicability to other (mathematical) domains and educational levels.
 
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