Editing Cognitive tutor (section)

== Theoretical foundations ==

=== Four-component architecture ===

[[Intelligent tutoring system|Intelligent tutoring systems (ITS)]] have a four-component architecture: a domain model, a student model, a tutoring model<ref>{{cite journal|author=Self, J.|year=1990|title=Theoretical Foundations for Intelligent Tutoring Systems|journal=Journal of Artificial Intelligence in Education |volume=1|issue=4|pages=3–14|citeseerx=10.1.1.53.6841}}</ref> and an interface component.

The domain model contains the rules, concepts, and knowledge related to the domain to be learned. It helps to evaluate students' performance and detect students' errors by setting a standard of domain expertise.{{Citation needed|date=October 2023}}

The student model, the central component of an ITS, is expected to contain knowledge about the students: their cognitive and affective states, and their progress as they learn. The function of the student model is threefold: to gather data from and about the learner, to represent the learner's knowledge and learning process, and to perform diagnostics of a student's knowledge and select optimal pedagogical strategies.<ref>{{cite book|author=Wenger, E.|year=1987|title=Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge|url=https://archive.org/details/artificialintell00weng|url-access=registration|publisher=Morgan Kaufmann Publishers Inc.|location=Los Altos|isbn=978-0-934613-26-2}}</ref>

The tutoring model uses the data gained from the domain model and student model to make decisions about tutoring strategies such as whether or not to intervene, or when and how to intervene. Functions of the tutoring model include instruction delivery and content planning.<ref name="Nkambou">{{Cite book |editor=Roger Nkambou |editor2=Jacqueline Bourdeau |editor3=Riichiro Mizoguchi |year=2010|title=Advances in Intelligent Tutoring Systems |page=308|publisher=Springer |isbn=978-3-642-14362-5}}</ref>

The interface component reflects the decisions made by the tutoring model in different forms such as Socratic dialogs, feedback and hints. Students interact with the tutor through the learning interface, also known as communication. The interface provides domain knowledge elements.<ref name="Nkambou"/>

=== Cognitive model ===

A ''cognitive model'' replicates the domain knowledge and skills comparable to that of a human expert or an advanced student of the domain. A [[cognitive model]] enables intelligent tutoring systems to respond to problem-solving situations in a way similar to a human tutor.<ref>{{cite book|author=Corbett, A.T.|author2=Koedinger, K.R.|author3=Anderson, J.R.|year=1997|chapter=Intelligent Tutoring Systems|editor1=Helander, T.K.|editor2=Landauer, P.|title=Handbook of Human-Computer Interaction|publisher=Elsevier Science|location=Amsterdam}}</ref> A tutoring system adopting a cognitive model is called a cognitive tutor.

A cognitive model is an expert system that generates a multitude of solutions to the problems presented to students. The cognitive model is used to trace each student's solution through complex alternative solution paths, enabling the tutor to provide step-by-step feedback and advice, and to maintain a targeted model of the student's knowledge based on student performance.<ref>{{cite journal|author=Corbett, A.|author2=Kauffman, L.|author3=MacLaren, B.|author4=Wagner, A.|author5=Jones, E.|year=2010|title=A Cognitive Tutor for genetics problem solving: Learning gains and student modeling|journal=Journal of Educational Computing Research|volume=42|issue=2|pages=219–239|url=https://www.cs.cmu.edu/~genetics/Research/2010JEdCompRes.pdf|doi=10.2190/EC.42.2.e|s2cid=62695743}}</ref>

=== Cognitive Tutors ===

Cognitive Tutors provide step-by-step guidance as a learner develops a complex problem-solving skill through practice.<ref>{{cite journal|author=VanLehn, K.|year=2006|title=The behavior of tutoring systems|journal=International Journal of Artificial Intelligence in Education|volume=16|issue=3|pages=227–265|url=http://www.learnlab.org/opportunities/summer/readings/06IJAIED.pdf}}</ref> Typically, cognitive tutors provide such forms of support as: (a) a problem-solving environment that is designed rich and "thinking visible"; (b) step-by-step feedback on student performance; (c) feedback messages specific to errors; (d) context-specific next-step hints at student's request, and (e) individualized problem selection.<ref name="Aleven, 2010"/>

Cognitive Tutors accomplish two of the principal tasks characteristic of human tutoring: (1) monitors the student's performance and providing context-specific individual instruction, and (2) monitors the student's learning and selects appropriate problem-solving activities.<ref name="Koedinger">{{cite book|author=Koedinger, K.R.|author2=Corbett, A.T.|year=2006|chapter=Cognitive tutors: Technology bringing learning sciences to the classroom|editor=Sawyer, R.K.|title=The Cambridge handbook of the learning sciences|publisher=Cambridge University Press|location=New York|url=http://learnlab.org/uploads/mypslc/publications/koedingercorbett06.pdf}}</ref>

Both cognitive model and two underlying algorithms, model tracing and knowledge tracing, are used to monitor the student's learning. In model tracing, the cognitive tutor uses the cognitive model in complex problems to follow the student's individual path and provide prompt accuracy feedback and context-specific advice. In knowledge tracing, the cognitive tutor uses a [[Bayesian Knowledge Tracing]] method of evaluating the student's knowledge and uses this student model to select appropriate problems for each student.<ref name="Koedinger"/>

=== Cognitive architecture ===

Cognitive tutor development is guided by [[ACT-R]] [[cognitive architecture]], which specifies the underlying framework developing the cognitive model or expert component of a cognitive tutor.

[[ACT-R]], a member of the [[Adaptive Control of Thought|ACT]] family, is the most recent cognitive architecture, devoted primarily to modelling human behavior. ACT-R includes a [[declarative memory]] of factual knowledge and a [[procedural memory]] of production rules. The architecture functions by matching productions on perceptions and facts, mediated by the real-valued activation levels of objects, and executing them to affect the environment or alter declarative memory. ACT-R has been used to model psychological aspects such as memory, attention, reasoning, problem solving, and language processing.<ref>P. Langley and J. E. Laird, "Cognitive Architectures: Research Issues and Challenges". Draft of October 31, 2002.</ref>