Open AccessProceedings Article
Procedural help in Andes: generating hints using a Bayesian network student model
Abigail S. Gertner,Cristina Conati,Kurt VanLehn +2 more
- pp 106-111
Reads0
Chats0
TLDR
Andes, an intelligent tutoring system for Newtonian physics, refers to a probabilistic student model to make decisions about responding to help requests, and provides feedback and hints tailored to the student's knowledge and goals.Abstract:
One of the most important problems for an intelligent tutoring system is deciding how to respond when a student asks for help. Responding cooperatively requires an understanding of both what solution path the student is pursuing, and the student's current level of domain knowledge. Andes, an intelligent tutoring system for Newtonian physics, refers to a probabilistic student model to make decisions about responding to help requests. Andes' student model uses a Bayesian network that computes a probabilistic assessment of three kinds of information: (I) the student's general knowledge about physics, (2) the student's specific knowledge about the current problem, and (3) the abstract plans that the student may be pursuing to solve the problem. Using this model, Andes provides feedback and hints tailored to the student's knowledge and goals.read more
Citations
More filters
Book
Knowing What Students Know: The Science and Design of Educational Assessment
TL;DR: In this article, the authors propose a new kind of assessment called Knowing What Students Know (KSS), which aims to make as clear as possible the nature of students' accomplishments and the progress of their learning.
Proceedings ArticleDOI
The Andes Physics Tutoring System: Lessons Learned
Kurt VanLehn,Collin F. Lynch,Kay G. Schulze,Joel A. Shapiro,Robert Shelby,Linwood Taylor,Don Treacy,Anders Weinstein,Mary C. Wintersgill +8 more
TL;DR: The Andes system demonstrates that student learning can be significantly increased by upgrading only their homework problem-solving support, and its key feature appears to be the grain-size of interaction.
Journal ArticleDOI
Using Bayesian Networks to Manage Uncertainty in Student Modeling
TL;DR: The basic mechanisms that allow Andes’ student models to soundly perform assessment and plan recognition, as well as the Bayesian network solutions to issues that arose in scaling up the model to a full-scale, field evaluated application are described.
Book
Building Intelligent Interactive Tutors: Student-centered Strategies for Revolutionizing E-learning
TL;DR: Building Intelligent Interactive Tutors discusses educational systems that assess a student's knowledge and are adaptive to a students' learning needs, and taps into 20 years of research on intelligent tutors to bring designers and developers a broad range of issues and methods that produce the best intelligent learning environments possible.
Journal ArticleDOI
Intelligent tutoring systems with conversational dialogue
TL;DR: A new generation of intelligent tutoring systems that hold mixed-initiative conversational dialogues with the learner, and help students actively construct knowledge through conversations are presented.
References
More filters
Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Journal ArticleDOI
Cognitive Tutors: Lessons Learned
TL;DR: The 10-year history of tutor development based on the advanced computer tutoring (ACT) theory is reviewed, finding that a new system for developing and deploying tutors is being built to achieve the National Council of Teachers of Mathematics (NCTM) standards for high-school mathematics in an urban setting.
Journal ArticleDOI
A Bayesian model of plan recognition
TL;DR: It is argued that the problem of plan recognition, inferring an agent's plan from observations, is largely a problem of inference under conditions of uncertainty and an approach to the plan recognition problem that is based on Bayesian probability theory is presented.
Journal ArticleDOI
A model of the self-explanation effect.
TL;DR: Computational experiments indicate that Cascade's learning mechanisms are jointly sufficient to reproduce the self-explanation effect, and a computer model is described, Cascade, that accounts for these findings.
Journal ArticleDOI
Effective Tutoring Techniques: A Comparison of Human Tutors and Intelligent Tutoring Systems
TL;DR: The types of guidance and support provided by human tutors with those in intelligent tutoring systems are compared, by examining the process of recovering from impasses encountered during problem solving.