5,506 research outputs found
An adaptation algorithm for an intelligent natural language tutoring system
The focus of computerised learning has shifted from content delivery towards personalised online learning with Intelligent Tutoring Systems (ITS). Oscar Conversational ITS (CITS) is a sophisticated ITS that uses a natural language interface to enable learners to construct their own knowledge through discussion. Oscar CITS aims to mimic a human tutor by dynamically detecting and adapting to an individual's learning styles whilst directing the conversational tutorial. Oscar CITS is currently live and being successfully used to support learning by university students. The major contribution of this paper is the development of the novel Oscar CITS adaptation algorithm and its application to the FelderāSilverman learning styles model. The generic Oscar CITS adaptation algorithm uniquely combines the strength of an individual's learning style preference with the available adaptive tutoring material for each tutorial question to decide the best fitting adaptation. A case study is described, where Oscar CITS is implemented to deliver an adaptive SQL tutorial. Two experiments are reported which empirically test the Oscar CITS adaptation algorithm with students in a real teaching/learning environment. The results show that learners experiencing a conversational tutorial personalised to their learning styles performed significantly better during the tutorial than those with an unmatched tutorial
Personalising Learning with Dynamic Prediction and Adaptation to Learning Styles in a Conversational Intelligent Tutoring System
This thesis presents research that combines the benefits of intelligent tutoring
systems (ITS), conversational agents (CA) and learning styles theory by constructing
a novel conversational intelligent tutoring system (CITS) called Oscar. Oscar CITS
aims to imitate a human tutor by implicitly predicting individualsā learning style
preferences and adapting its tutoring style to suit them during a tutoring
conversation.
ITS are computerised learning systems that intelligently personalise tutoring
based on learner characteristics such as existing knowledge and learning style. ITS
are traditionally student-led, hyperlink-based learning systems that adapt the
presentation of learning resources by reordering or hiding links. Research suggests
that students learn more effectively when instruction matches their learning style,
which is typically modelled explicitly using questionnaires or implicitly based on
behaviour. Learning is a social process and natural language interfaces to ITS, such
as CAs, allow students to construct knowledge through discussion. Existing CITS
adapt tutoring according to student knowledge, emotions and mood, however no
CITS adapts to learning styles.
Oscar CITS models a human tutor by directing a tutoring conversation and
automatically detecting and adapting to an individualās learning styles. Original
methodologies and architectures were developed for constructing an Oscar Predictive
CITS and an Oscar Adaptive CITS. Oscar Predictive CITS uses knowledge captured
from a learning styles model to dynamically predict learning styles from an
individualās tutoring dialogue. Oscar Adaptive CITS applies a novel adaptation
algorithm to select the best tutoring style for each tutorial question. The Oscar CITS
methodologies and architectures are independent of the learning styles model and
subject domain. Empirical studies involving real students have validated the
prediction and adaptation of learning styles in a real-world teaching/learning
environment. The results show that learning styles can be successfully predicted
from a natural language tutoring dialogue, and that adapting the tutoring style
significantly improves learning performance
Adapting Progress Feedback and Emotional Support to Learner Personality
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Optimising ITS behaviour with Bayesian networks and decision theory
We propose and demonstrate a methodology for building tractable normative intelligent tutoring systems (ITSs). A normative ITS uses a Bayesian network for long-term student modelling and decision theory to select the next tutorial action. Because normative theories are a general framework for rational behaviour, they can be used to both define and apply learning theories in a rational, and therefore optimal, way. This contrasts to the more traditional approach of using an ad-hoc scheme to implement the learning theory. A key step of the methodology is the induction and the continual adaptation of the Bayesian network student model from student performance data, a step that is distinct from other recent Bayesian net approaches in which the network structure and probabilities are either chosen beforehand by an expert, or by efficiency considerations. The methodology is demonstrated by a description and evaluation of CAPIT, a normative constraint-based tutor for English capitalisation and punctuation. Our evaluation results show that a class using the full normative version of CAPIT learned the domain rules at a faster rate than the class that used a non-normative version of the same system
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