| Summary: | In this thesis, we present an artificial
intelligence approach for tutoring students in environments
where there is a large repository of possible learning objects
(e.g. texts, videos).
In particular, we advocate that students learn on the
basis of past experiences of peers.
This aligns with McCalla's proposed ecological approach
for intelligent tutoring, where
a learning object's history-of-use is retained and leveraged
to instruct future students.
We offer three distinct models
that serve to deliver the required intelligent tutoring:
(i) a curriculum sequencing algorithm selecting which learning
objects to present to students based on benefits to knowledge
obtained by similar peers
(ii) a framework for peers to provide commentary on the learning
objects they've experienced (annotations) together with an algorithm
for reasoning about which annotations to present to students
that incorporates modeling trust in annotators (i.e. their reputation)
and ratings provided by students (votes for and against) for the
annotations they have been shown
(iii) an opportunity for peers to guide the growth of the corpus
by proposing divisions of current objects, together with an
algorithm for reasoning about which of these new objects should
be offered to students in order to enhance their learning.
All three components are validated as beneficial in improving the learning of students. This is first of all achieved through
a novel approach of simulated student learning, designed to
enable the tracking of the experiences of a very large number of
peers with an extensive repository of objects,
through the effective modeling of knowledge gains.
This is also coupled with a preliminary study with human participants
that confirms the value of our framework.
In all, we offer a rich and varied role for peers in guiding the
learning of students in intelligent tutoring environments,
made possible by careful modeling of the students who are being
taught and of the potential benefits to learning that would
be derived with the selection of appropriate tutorial content.
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