Intelligent Tutors for All: The Constraint-Based Approach

Mitrović, Antonija; Martin, Brent; Suraweera, Pramuditha

doi:10.1109/mis.2007.74

Cited by 95 publications

(45 citation statements)

References 6 publications

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“…The constraint-based tutoring paradigm [16,17,23] simplifies ITS development by focusing on conditions that should hold for correct solutions, rather than defining how to reach such a solution. In this paradigm, constraints have three parts: a relevance condition (when is it applicable), a satisfaction condition (what should hold), and a feedback message that is reported if the constraint is violated.…”

Section: Model-tracing Tutors (Based On Procedures) the Extensible Pmentioning

confidence: 99%

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An Extensible Domain-Specific Language for Describing Problem-Solving Procedures

Heeren

Jeuring

2017

Lecture Notes in Computer Science

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Abstract. An intelligent tutoring system (ITS) is often described as having an inner loop for supporting solving tasks step by step, and an outer loop for selecting tasks. Many task domains have problem-solving procedures that express how tasks can be solved by applying steps or rules in a controlled way. In this paper we collect established ITS design principles, and use the principles to compare and evaluate existing ITS paradigms with respect to the way problem-solving procedures are specified. We argue that problem-solving procedures need an explicit representation, which is missing in most ITSs. We present an extensible domain-specific language (DSL) that provides a rich vocabulary for accurately describing procedures. We give three examples of tutors from di↵erent task domains that illustrate our DSL approach and highlight important qualities such as modularity, extensibility, and reusability.

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Section: Model-tracing Tutors (Based On Procedures) the Extensible Pmentioning

confidence: 99%

“…In particular, there are model-tracing tutors [2], example-tracing tutors [1], constraint-based tutors [16], and data-driven tutors [14]. A significant di↵erence between the paradigms lies in the way the expert knowledge necessary for following the steps of a student, is specified.…”

Section: Introductionmentioning

confidence: 99%

An Extensible Domain-Specific Language for Describing Problem-Solving Procedures

Heeren

Jeuring

2017

Lecture Notes in Computer Science

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“…The resulting architecture, which divides an intelligent tutoring systems into four conceptual components, is still used today, see for example the recent Advances on Intelligent Tutoring Systems [43]. The architecture is agnostic of the approach an intelligent tutoring system takes to representing expert knowledge: both modeltracing tutors [2] and constraint-based tutors [40] are described using the same architecture. The architecture has been used to develop stand-alone intelligent tutoring systems as well as web-based solutions [1].…”

Section: Related Workmentioning

confidence: 99%

Feedback services for stepwise exercises

Heeren

Jeuring

2014

Science of Computer Programming

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Advanced learning environments such as intelligent tutoring systems for algebra, logic, programming, physics, etc. let a student practice with stepwise exercises, and support a student solving such exercises by providing feedback. These environments usually provide various types of feedback, for example about the correctness of a step, common errors, hints about how to proceed, or complete worked-out solutions. Calculating feedback is generally delegated to a dedicated expert knowledge module, also known as a domain reasoner. Existing architectural descriptions of learning environments do not precisely specify the interaction between this module and the rest of the learning system. We propose a design based on the stateless client-server architecture that clearly decouples the expert knowledge module from the learning environment. We describe a set of feedback services that support the inner (interactions within an exercise) and outer (over a collection of exercises) loops of a learning system, and that provide meta-information about a class of exercises, such as solving quadratic equations, or performing Gaussian elimination. The feedback services do not depend on a particular domain and are based on the various feedback types described in the literature.The paper analyzes which domain-specific knowledge about an exercise class is needed for implementing the feedback services. Based on this analysis, we developed a framework for implementing domain reasoners that offers generic functionality such as rewriting, simplifying, and comparing terms. We have implemented several domain reasoners in this framework, both for external learning environments and for simple prototypes. The proposed design is evaluated with these implementations, and we reflect on our experience with developing domain reasoners.

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“…ITSs have proven their effectiveness not only in controlled lab studies, but also in real classrooms [2,3,5,6,7]. These computer-based tutors achieve significant improvements in comparison to classroom learning due to their fine-grained knowledge of the instructional domain, their ability to employ various pedagogical strategies, and their student modelling capabilities which enables individualized instruction.…”

Section: Related Workmentioning

confidence: 99%

“…Intelligent Tutoring Systems (ITS) are knowledge-based systems that simulate the behaviour of human teachers [2,3,4]. ITSs aim to provide an experience similar to personal tutoring without the need for human intervention.…”

Section: Introductionmentioning

confidence: 99%

Thermo-Tutor: An Intelligent Tutoring System for thermodynamics

Mitrović

Williamson

Bebbington

et al. 2011

2011 IEEE Global Engineering Education Conference (EDUCON)

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Abstract-We present the design and an evaluation of ThermoTutor, an Intelligent Tutoring System (ITS) that teaches thermodynamic cycles in closed systems. Thermo-Tutor provides opportunities for students to practice their skills by solving problems. When a student submits a solution, Thermo-Tutor analyzes it and provides appropriate feedback. We discuss the support for problem solving, and the student model the ITS maintains. An initial evaluation of Thermo-Tutor was performed at the University of Canterbury. The findings show that the ITS supports student learning effectively.

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Intelligent Tutors for All: The Constraint-Based Approach

Cited by 95 publications

References 6 publications

An Extensible Domain-Specific Language for Describing Problem-Solving Procedures

An Extensible Domain-Specific Language for Describing Problem-Solving Procedures

Feedback services for stepwise exercises

Thermo-Tutor: An Intelligent Tutoring System for thermodynamics

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