Cognitive Tutor: Applied research in mathematics education

Ritter, Steven; Anderson, John R.; Koedinger, Kenneth R.; Corbett, Albert T.

doi:10.3758/bf03194060

Cited by 314 publications

(217 citation statements)

References 25 publications

Supporting

Mentioning

194

Contrasting

Unclassified

Order By: Relevance

“…Since then the work has greatly expanded in its dissemination-over 500,000 students a year use the Cognitive Tutor Algebra course (e.g., Ritter, Anderson, Koedinger, & Corbett, 2007)-and in its scope-Cognitive Tutors and variations thereof have been created for a wide variety of content, including intercultural competence (Ogan, Aleven, & Jones, 2010), statistics (Lovett, Meyer, & ille, 2008), chemistry (Yaron et al, 2010), and genetics (Corbett, Kauff man, MacLaren, Wagner, & Jones, 2010). Many large-scale evaluations of these courses have demonstrated their eff ectiveness (Ritter, Kulikowich, Lei, McGuire, & Morgan, 2007).…”

Section: Testing Knowledge Component Models In Instructional Contextsmentioning

confidence: 99%

Learning to Think: Cognitive Mechanisms of Knowledge Transfer

Koedinger

Roll

2012

The Oxford Handbook of Thinking and Reasoning

Self Cite

View full text Add to dashboard Cite

Learning to think is about transfer. The scope of transfer is essentially a knowledge representation question. Experiences during learning can lead to alternative latent representations of the acquired knowledge, not all of which are equally useful. Productive learning facilitates a general representation that yields accurate behavior in a large variety of new situations, thus enabling transfer. This chapter explores two hypotheses. First, learning to think happens in pieces and these pieces, or knowledge components, are the basis of a mechanistic explanation of transfer. This hypothesis yields an instructional engineering prescription: that scientific methods of cognitive task analysis can be used to discover these knowledge components, and the resulting cognitive models can be used to redesign instruction so as to foster better transfer. The second hypothesis is that symbolic languages act as agents of transfer by focusing learning on abstract knowledge components that can enhance thinking across a wide variety of situations. The language of algebra is a prime example and we use it to illustrate (1) that cognitive task analysis can reveal knowledge components hidden to educators; (2) that such components may be acquired, like first language grammar rules, implicitly through practice; (3) that these components may be "big ideas" not in their complexity but in terms of their usefulness as they produce transfer across contexts; and (4) that domain-specific knowledge analysis is critical to effective application of domain-general instructional strategies.

show abstract

Section: Testing Knowledge Component Models In Instructional Contextsmentioning

confidence: 99%

Learning to Think: Cognitive Mechanisms of Knowledge Transfer

Koedinger

Roll

2012

The Oxford Handbook of Thinking and Reasoning

Self Cite

View full text Add to dashboard Cite

show abstract

“…We used the Cognitive Tutor as a control condition. Since its effectiveness is well known [9], we conjectured that this comparison would provide a good sense of the effectiveness of learning by teaching relative to tutored-problem solving. We targeted a unit of the Cognitive Tutor where students learn to solve equations with variables on both sides.…”

Section: Methodsmentioning

confidence: 99%

“…In both schools, the Algebra I Cognitive Tutor [9] (referred to as the Cognitive Tutor or CT hereafter) is intensively used. There are two Algebra I classes in one school (N=40), and two Algebra I (N=30) and two Algebra II (N=34) classes in another school.…”

Section: Methodsmentioning

confidence: 99%

Learning by Teaching SimStudent – An Initial Classroom Baseline Study Comparing with Cognitive Tutor

Matsuda

Yarzebinski

Keiser

et al. 2011

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

This paper describes an application of a machine-learning agent, SimStudent, as a teachable peer learner that allows a student to learn by teaching. SimStudent has been integrated into APLUS (Artificial Peer Learning environment Using SimStudent), an on-line game-like learning environment. The first classroom study was conducted in local public high schools to test the effectiveness of APLUS for learning linear algebra equations. In the study, learning by teaching (i.e., APLUS) was compared with learning by tutored-problem solving (i.e., Cognitive Tutor). The results show that the prior knowledge has a strong influence on tutor learning -for students with insufficient training on the target problems, learning by teaching may have limited benefits compared to learning by tutored problem solving. It was also found that students often use inappropriate problems to tutor SimStudent that did not effectively facilitate the tutor learning.

show abstract

“…Such activities are often multistep problems to solve, but may also include interactions in a simulation, game, or a dialogue. Figure 2 shows an example of a complex activity selected from an algebra curriculum unit on systems of linear equations where students use table, graphical, and symbolic representations to model a problem scenario and answer questions about it (Ritter et al 2007). Once an activity is selected, the inner loop takes over and, as shown in figure 1, persists until the student has completed the activity.…”

Section: A Summary and Illustration Of The Key Functions Of Intelligementioning

confidence: 99%

“…These models are then used to generate hints and feedback (inner loop of figure 1). In particular, two classes of effective tutors, cognitive tutors (for example, Ritter et al [2007]) and constraint-based tutors (for example, Mitrovic [2003]), rely on knowledge representations, "production rules" or "constraints," that require extensive programming, expertise, and often empirical research to develop. In contrast, data-driven methods can enable more rapid development of new intelligent tutoring systems.…”

Section: Machine Learning Andmentioning

confidence: 99%

New Potentials for Data‐Driven Intelligent Tutoring System Development and Optimization

et al. 2013

Self Cite

View full text Add to dashboard Cite

Increasing widespread use of educational technologies is producing vast amounts of data. Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for data-driven development and optimization of educational technologies, focusing on intelligent tutoring systems We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference.

show abstract

Cognitive Tutor: Applied research in mathematics education

Cited by 314 publications

References 25 publications

Learning to Think: Cognitive Mechanisms of Knowledge Transfer

Learning to Think: Cognitive Mechanisms of Knowledge Transfer

Learning by Teaching SimStudent – An Initial Classroom Baseline Study Comparing with Cognitive Tutor

New Potentials for Data‐Driven Intelligent Tutoring System Development and Optimization

Contact Info

Product

Resources

About