Book ChapterDOI
Andes: A Coached Problem Solving Environment for Physics
Abigail S. Gertner,Kurt VanLehn +1 more
- pp 133-142
TLDR
This paper gives an overview of Andes, focusing on the overall architecture and the student's experience using the system.Abstract:
Andes is an Intelligent Tutoring System for introductory college physics. The fundamental principles underlying the design of Andes are: (1) encourage the student to construct new knowledge by providing hints that require them to derive most of the solution on their own, (2) facilitate transfer from the system by making the interface as much like a piece of paper as possible, (3) give immediate feedback after each action to maximize the opportunities for learning and minimize the amount of time spent going down wrong paths, and (4) give the student flexibility in the order in which actions are performed, and allow them to skip steps when appropriate. This paper gives an overview of Andes, focusing on the overall architecture and the student's experience using the system.read more
Citations
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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.
Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering.
TL;DR: The National Research Council's Discipline-Based Education Research (DBER) report (National Research Council, 2012) captures the state-of-theart advances in our understanding of engineering and science student learning and highlights commonalities with other science-based education research programs as mentioned in this paper.
Journal ArticleDOI
Evaluating Bayesian networks' precision for detecting students' learning styles
TL;DR: The proposed Bayesian model was evaluated in the context of an Artificial Intelligence Web-based course and the results obtained are promising as regards the detection of students' learning styles.
References
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Journal ArticleDOI
Categorization and Representation of Physics Problems by Experts and Novices
TL;DR: Results from sorting tasks and protocols reveal that experts and novices begin their problem representations with specifiably different problem categories, and completion of the representations depends on the knowledge associated with the categories.
Proceedings ArticleDOI
Intelligent Tutoring Goes To School in the Big City
TL;DR: This study provides further evidence that laboratory tutoring systems can be scaled up and made to work, both technically and pedagogically, in real and unforgiving settings like urban high schools.
Journal ArticleDOI
Learning to think like a physicist: A review of research‐based instructional strategies
TL;DR: This article reported the preliminary results of using these strategies in introductory physics courses that emphasize problem-solving, and reported that the results showed that these strategies were effective in many introductory courses.
Book ChapterDOI
On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks
TL;DR: The knowledge structures represented in the student model are described and the implementation of the Bayesian network assessor is discussed, and a preliminary evaluation of the time performance of stochastic sampling algorithms to update the network is presented.
Journal ArticleDOI
Prescribing Effective Human Problem-Solving Processes: Problem Description in Physics
Joan I. Heller,Frederick Reif +1 more
TL;DR: In this paper, the authors formulate a theoretical model specifying the underlying knowledge and procedures whereby human problem solvers can generate useful initial descriptions of scientific problems, and test such a model, formulated for the domain of mechanics, devised a carefully controlled experiment where human subjects were induced to act in accordance with specified alternative models and where their resulting performance was observed in detail.