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Identifier |
000457369 |
Title |
Knowledge representation for affective and adaptive tutoring systems |
Alternative Title |
Αναπαράσταση γνώσης σε συναισθηματικά και προσαρμοστικά συστήματα μάθησης |
Author
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Δούγαλης, Αχιλλεύς-Νικόλαος Β.
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Thesis advisor
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Πλεξουσάκης, Δημήτριος
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Reviewer
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Αναστασιάδης, Παναγιώτης
Αργυρός, Αντώνιος
Καλογιαννάκης, Μιχαήλ
Πάτκος, Θεόδωρος
Στεφανίδης, Κωνσταντίνος
Τσαμαρδινός, Ιωάννης
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Abstract |
In recent years, there has been a meteoric rise in the use of educational technology. Intelligent
Tutoring Systems (ITS) and Affective Tutoring Systems (ATS) are computer systems that can
successfully teach courses to users at all levels of education, using AI techniques (the former) and
Affect Sensing technologies (the latter) in order to facilitate learning. These systems are typically
built around a specific subject such as algebra or genetics,etc, making reusability in different
domains difficult.
In contrast to ITS, Adaptive Learning Systems are more domain-agnostic, being able to teach more
than one course. Adaptive Learning Systems concentrate on the presentation of a course by
adapting it to the user’s learning preferences. These adaptations are based on learning theories that
classify learners according to the way they learn; these theories are known as Learning Styles. Two
major disadvantages of these systems is that they do not offer any type of feedback to the user
(emotional or cognitive), and that there are doubts in the scientific community concerning the
validity of Learning Styles Theories due to lack of relevant scientific evidence.
Massive Online Open Courses (MOOCs) are the latest word in educational technologies; they deal
with the problem of integrating many courses, users and instructors in a single platform. In order to
be able to build better MOOC platforms, a course-teaching system should combine the advantages
of ITS, ATS and Adaptive Learning Systems. Also, there is a need for a unified model that will be
able to adequately represent a range of different courses. Finally this system should have the ability
to calibrate its teaching strategies during interaction with users.
This doctoral dissertation describes the design, implementation and evaluation of AffLog (affective
Logic) Tutor, an ATS with the following specifications: The domain, tutoring and student models
are designed using the Predicate Calculus and the Event Calculus. They are then implemented
following the Answer Set Programming (ASP) formalism using the programming language Clingo.
Also, the student model contains information about the user’s learning style according to the
Felder-Silverman model. AffLog uses AI methods such as Planning and Projection in order to select
the most suitable parts of the course for the current user according to the user's learning style and
suitably present these parts to the user. The affective model of the system is designed to react to the
user’s current emotional state providing advice and encouragement, thus facilitating the learning
process.
In order to evaluate the system, a simple course containing instructions on how to play the “Settlers
of Catan” board game was designed and implemented. Evaluation of the system showed that users
had high learning gains from their interaction with the system.
Also, in an effort to find if Learning Styles are relevant to AffLog’s learning gains, a second ATS
system, AffLogRL was developed. AffLogRL is similar to AffLog except that the latter replaces all
modules that make use of Learning Styles with a Reinforcement learning agent. This agent uses the
experience it accumulates by interacting with the users in order to formulate a teaching policy.
After the training of AffLogRL with over 100 human users, the system was evaluated. The results
showed that AffLogRL performed similarly to the AffLog system indicating that with more training
and using Relational approaches the RL will perform better.
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Language |
English |
Subject |
Adaptive Learning Systems |
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Affective Tutoring Systems |
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Answer Set Programming |
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Learning styles |
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Reinforcement Learning |
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Ενισχυτική Μάθηση |
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Μέθοδοι μάθησης |
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Συναισθηματικά συστήματα διδασκαλίας |
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Συστήματα προσαρμοζόμενης εκμάθησης |
Issue date |
2023-07-21 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Doctoral theses
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Type of Work--Doctoral theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/8/a/0/metadata-dlib-1689673272-696067-8458.tkl
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Views |
912 |