Skill-oriented task sequencing in an intelligent tutor for basic algebra

McArthur, David J.; Stasz, Cathy; Hotta, John Y.; Peter, Orli; Burdorf, Christopher

doi:10.1007/bf00056218

Cited by 31 publications

(11 citation statements)

References 15 publications

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“…Some adaptive task selection has been done heuristically using a variety of factors besides difficulty. (Hosseini, Hsiao, Guerra, & Brusilovsky, 2015;McArthur, Stasz, Hotta, Peter, & Burdorf, 1989;Melis et al, 2001;Pek & Poh, 2000a, 2000bVizcaino, Contreras, Favela, & Prieto, 2000).…”

Section: Prior Work On Adaptive Task Selectionmentioning

confidence: 96%

Adaptively selecting biology questions generated from a semantic network

Zhang

VanLehn

2016

Interactive Learning Environments

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The paper describes a biology tutoring system with adaptive question selection. Questions were selected for presentation to the student based on their utilities, which were estimated from the chance that the student's competence would increase if the questions were asked. Competence was represented by the probability of mastery of a set of biology knowledge components. Tasks were represented and selected based on which knowledge components they addressed. Unlike earlier work, where the knowledge components and their relationships to the questions were defined by domain experts, this project demonstrated that the knowledge components, questions and their relationships could all be generated from a semantic network. An experiment found that students using our adaptive question selection had reliably larger learning gains than students who received questions in a maladaptive order. ARTICLE HISTORY

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Section: Prior Work On Adaptive Task Selectionmentioning

confidence: 96%

Adaptively selecting biology questions generated from a semantic network

Zhang

VanLehn

2016

Interactive Learning Environments

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“…McArthur et al [21] automatically generate algebra problems as part of an intelligent tutor for algebra. They first collect and annotate problems by what skills they use, such as isolating positive terms, and multiplying both sides of an equation by −1.…”

Section: Education and Intelligent Tutorsmentioning

confidence: 99%

A trace-based framework for analyzing and synthesizing educational progressions

Andersen

Gulwani

Popović

2013

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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A key challenge in teaching a procedural skill is finding an effective progression of example problems that the learner can solve in order to internalize the procedure. In many learning domains, generation of such problems is typically done by hand and there are few tools to help automate this process. We reduce this effort by borrowing ideas from test input generation in software engineering. We show how we can use execution traces as a framework for abstracting the characteristics of a given procedure and defining a partial ordering that reflects the relative difficulty of two traces. We also show how we can use this framework to analyze the completeness of expert-designed progressions and fill in holes. Furthermore, we demonstrate how our framework can automatically synthesize new problems by generating large sets of problems for elementary and middle school mathematics and synthesizing hundreds of levels for a popular algebra-learning game. We present the results of a user study with this game confirming that our partial ordering can predict user evaluation of procedural difficulty better than baseline methods.

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“…Curriculum sequencing is one of the key components in an intelligent tutoring system 19,27,30]. In our approach, the topics are represented in a dependency graph, with links representing the relationship between topics, which include prerequisite, co-requisite, related, and remedial.…”

Section: Curriculum Sequencingmentioning

confidence: 99%

An intelligent distributed environment for active learning

Shang

Shi

Chen

2001

J. Educ. Resour. Comput.

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Active learning is an e ective learning approach. In this paper, we present a n i n telligent a g e n t assisted environment for active learning. The system is to better support studentcentered, self-paced, and highly interactive learning approach. Students' learning-related pro les, such as learning styles and background knowledge, are used in selecting, organizing, and presenting learning materials. A new approach t o course content organization and delivery is being developed based on smart instructional components, which c a n b e i ntegrated into a wide range of courses. The system is being implemented using the prevalent I n ternet, Web, digital library, and multi-agent technologies.

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Skill-oriented task sequencing in an intelligent tutor for basic algebra

Cited by 31 publications

References 15 publications

Adaptively selecting biology questions generated from a semantic network

Adaptively selecting biology questions generated from a semantic network

A trace-based framework for analyzing and synthesizing educational progressions

An intelligent distributed environment for active learning

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