CGSC 2001 Lecture Notes - Lecture 10: Intelligent Tutoring System, Directed Acyclic Graph, Bayesian Network

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COGSCI April 2, 2015
Intelligent Tutoring Systems and
User Modelling
Comparing Modes of Instruction
Back in the 80’s, Bloom was exploring how different contexts influence what students learn
Bloom compared:
oClassroom instruction: one teacher for a group of students
oIndividualized human tutoring: a tutor works with one or two students in private
sessions
Findings: human tutoring resulted in significantly higher learning gains
oHuman tutoring resulted in performance increase of 2 grade levels over classroom
instruction
Unfortunately, human tutoring is not a scalable approach – cannot provide private tutor for
each student
Challenge: to find a means of instruction that approximates the benefits of human tutoring AND
can reach many students
oField of Intelligent Tutoring Systems
Intelligent Tutoring Systems (ITS)
Computer applications that aim to support student learning by providing individualized help and
instruction, tailored to a given student’s needs
ITS are based on the assumption that human tutors are effective because they adapt to
student’s needs:
oOffer tailored feedback, including elaborated help that targets student misconceptions
Scaffold the student towards the answer without giving it all away
oProvide emotional support (i.e., trying to migrate frustration and increase motivation)
oSelect problems for students to work on that fall in the student’s zone of proximal
development (not too easy, not too hard)
How do ITS Adapt?
To provide tailored feedback, ITS typically include the following 3 software components that
each have a specific role:
oExpert module: stores information about the domain the student is learning about (ex.
math)
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