The Experts below are selected from a list of 2079 Experts worldwide ranked by ideXlab platform
Roger Chang - One of the best experts on this subject based on the ideXlab platform.
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straight talk about riser tension and more
ASME 2009 28th International Conference on Ocean Offshore and Arctic Engineering, 2009Co-Authors: Roger ChangAbstract:One of most confusing issues in riser engineering is the riser tension. The infamous effective tension equation relates it to the so-called material tension with external and internal pressures. Controversy remains after numerous papers published trying to clarify the subject, because different interpretations were presented by different authors. Instead of explaining this ‘abstract’ equation mathematically using the Free Body Diagram and differential equation as done in the literatures, this paper presents a down-to-earth interpretation that follows the riser loading history which starts with the Effective Weight to re-derive the same equation. Four keys to solve the riser tension mystery are identified; they are the hydrostatic head pressure vs. applied pressure, pressure generates the pressure end cap load vs. none generated, the vertical (top-tensioned) riser vs. bent (catenary) riser, and the single string riser vs. multiple strings riser. Based on these four keys, this paper will address the difference between the effective tension and material tension and identify which tension is to be used in the stress calculation. Also presented in the paper is the driver-reactor theory developed to explain the tension load distribution among riser strings due to Poisson’s effect with the applied pressure.Copyright © 2009 by ASME
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straight talk about riser tension and more
ASME 2009 28th International Conference on Ocean Offshore and Arctic Engineering, 2009Co-Authors: Roger ChangAbstract:One of most confusing issues in riser engineering is the riser tension. The infamous effective tension equation relates it to the so-called material tension with external and internal pressures. Controversy remains after numerous papers published trying to clarify the subject, because different interpretations were presented by different authors. Instead of explaining this ‘abstract’ equation mathematically using the Free Body Diagram and differential equation as done in the literatures, this paper presents a down-to-earth interpretation that follows the riser loading history which starts with the Effective Weight to re-derive the same equation. Four keys to solve the riser tension mystery are identified; they are the hydrostatic head pressure vs. applied pressure, pressure generates the pressure end cap load vs. none generated, the vertical (top-tensioned) riser vs. bent (catenary) riser, and the single string riser vs. multiple strings riser. Based on these four keys, this paper will address the difference between the effective tension and material tension and identify which tension is to be used in the stress calculation. Also presented in the paper is the driver-reactor theory developed to explain the tension load distribution among riser strings due to Poisson’s effect with the applied pressure.Copyright © 2009 by ASME
Mostafa Amin-naseri - One of the best experts on this subject based on the ideXlab platform.
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StaticsTutor: Free Body Diagram Tutor for Problem Framing
Lecture Notes in Computer Science, 2014Co-Authors: Enruo Guo, Stephen B. Gilbert, John K. Jackman, Gloria Starns, Matthew Hagge, Leann Faidley, Mostafa Amin-naseriAbstract:In this paper, we compare pioneer methods of educational data mining field with recommender systems techniques for predicting student performance. Additionally, we study the importance of including students’ attempt time sequences of parameterized exercises. The approaches we use are Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), Bayesian Probabilistic Tensor Factorization (BPTF), and Bayesian Probabilistic Matrix Factorization (BPMF). The last two approaches are from the recommender system’s field.We approach the problem using question-level Knowledge Components (KCs) and test the methods using cross-validation. In this work, we focus on predicting students’ performance in parameterized exercises. Our experiments shows that advanced recommender system techniques are as accurate as the pioneer methods in predicting student performance. Also, our studies show the importance of considering time sequence of students’ attempts to achieve the desirable accuracy
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Intelligent Tutoring Systems - StaticsTutor: Free Body Diagram Tutor for Problem Framing
Intelligent Tutoring Systems, 2014Co-Authors: Enruo Guo, Stephen B. Gilbert, John K. Jackman, Gloria Starns, Matthew Hagge, Leann Faidley, Mostafa Amin-naseriAbstract:While intelligent tutoring systems have been designed to teach Free-Body Diagrams, existing software often forces students to define variables and equations that may not be necessary for conceptual understanding during the problem framing stage. StaticsTutor was developed to analyze solutions from a student-drawn Diagram and recognize misconceptions at the earliest stages of problem framing, without requiring numerical force values or the need to provide equilibrium equations. Preliminary results with 81 undergraduates showed that it detects several frequent misconceptions in statics and that students are interested in using it, though they have suggestions for improvement. This research offers insights in the development of a Diagram-based tutor to help problem framing, which can be generalized to tutors for other forms of Diagrams.
Enruo Guo - One of the best experts on this subject based on the ideXlab platform.
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staticstutor Free Body Diagram tutor for problem framing
Intelligent Tutoring Systems, 2014Co-Authors: Enruo Guo, Stephen B. Gilbert, John K. Jackman, Gloria Starns, Matthew Hagge, Leann Faidley, Mostafa AminnaseriAbstract:While intelligent tutoring systems have been designed to teach Free-Body Diagrams, existing software often forces students to define variables and equations that may not be necessary for conceptual understanding during the problem framing stage. StaticsTutor was developed to analyze solutions from a student-drawn Diagram and recognize misconceptions at the earliest stages of problem framing, without requiring numerical force values or the need to provide equilibrium equations. Preliminary results with 81 undergraduates showed that it detects several frequent misconceptions in statics and that students are interested in using it, though they have suggestions for improvement. This research offers insights in the development of a Diagram-based tutor to help problem framing, which can be generalized to tutors for other forms of Diagrams.
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StaticsTutor: Free Body Diagram Tutor for Problem Framing
Lecture Notes in Computer Science, 2014Co-Authors: Enruo Guo, Stephen B. Gilbert, John K. Jackman, Gloria Starns, Matthew Hagge, Leann Faidley, Mostafa Amin-naseriAbstract:In this paper, we compare pioneer methods of educational data mining field with recommender systems techniques for predicting student performance. Additionally, we study the importance of including students’ attempt time sequences of parameterized exercises. The approaches we use are Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), Bayesian Probabilistic Tensor Factorization (BPTF), and Bayesian Probabilistic Matrix Factorization (BPMF). The last two approaches are from the recommender system’s field.We approach the problem using question-level Knowledge Components (KCs) and test the methods using cross-validation. In this work, we focus on predicting students’ performance in parameterized exercises. Our experiments shows that advanced recommender system techniques are as accurate as the pioneer methods in predicting student performance. Also, our studies show the importance of considering time sequence of students’ attempts to achieve the desirable accuracy
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Intelligent Tutoring Systems - StaticsTutor: Free Body Diagram Tutor for Problem Framing
Intelligent Tutoring Systems, 2014Co-Authors: Enruo Guo, Stephen B. Gilbert, John K. Jackman, Gloria Starns, Matthew Hagge, Leann Faidley, Mostafa Amin-naseriAbstract:While intelligent tutoring systems have been designed to teach Free-Body Diagrams, existing software often forces students to define variables and equations that may not be necessary for conceptual understanding during the problem framing stage. StaticsTutor was developed to analyze solutions from a student-drawn Diagram and recognize misconceptions at the earliest stages of problem framing, without requiring numerical force values or the need to provide equilibrium equations. Preliminary results with 81 undergraduates showed that it detects several frequent misconceptions in statics and that students are interested in using it, though they have suggestions for improvement. This research offers insights in the development of a Diagram-based tutor to help problem framing, which can be generalized to tutors for other forms of Diagrams.
Stephen B. Gilbert - One of the best experts on this subject based on the ideXlab platform.
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staticstutor Free Body Diagram tutor for problem framing
Intelligent Tutoring Systems, 2014Co-Authors: Enruo Guo, Stephen B. Gilbert, John K. Jackman, Gloria Starns, Matthew Hagge, Leann Faidley, Mostafa AminnaseriAbstract:While intelligent tutoring systems have been designed to teach Free-Body Diagrams, existing software often forces students to define variables and equations that may not be necessary for conceptual understanding during the problem framing stage. StaticsTutor was developed to analyze solutions from a student-drawn Diagram and recognize misconceptions at the earliest stages of problem framing, without requiring numerical force values or the need to provide equilibrium equations. Preliminary results with 81 undergraduates showed that it detects several frequent misconceptions in statics and that students are interested in using it, though they have suggestions for improvement. This research offers insights in the development of a Diagram-based tutor to help problem framing, which can be generalized to tutors for other forms of Diagrams.
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StaticsTutor: Free Body Diagram Tutor for Problem Framing
Lecture Notes in Computer Science, 2014Co-Authors: Enruo Guo, Stephen B. Gilbert, John K. Jackman, Gloria Starns, Matthew Hagge, Leann Faidley, Mostafa Amin-naseriAbstract:In this paper, we compare pioneer methods of educational data mining field with recommender systems techniques for predicting student performance. Additionally, we study the importance of including students’ attempt time sequences of parameterized exercises. The approaches we use are Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), Bayesian Probabilistic Tensor Factorization (BPTF), and Bayesian Probabilistic Matrix Factorization (BPMF). The last two approaches are from the recommender system’s field.We approach the problem using question-level Knowledge Components (KCs) and test the methods using cross-validation. In this work, we focus on predicting students’ performance in parameterized exercises. Our experiments shows that advanced recommender system techniques are as accurate as the pioneer methods in predicting student performance. Also, our studies show the importance of considering time sequence of students’ attempts to achieve the desirable accuracy
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Intelligent Tutoring Systems - StaticsTutor: Free Body Diagram Tutor for Problem Framing
Intelligent Tutoring Systems, 2014Co-Authors: Enruo Guo, Stephen B. Gilbert, John K. Jackman, Gloria Starns, Matthew Hagge, Leann Faidley, Mostafa Amin-naseriAbstract:While intelligent tutoring systems have been designed to teach Free-Body Diagrams, existing software often forces students to define variables and equations that may not be necessary for conceptual understanding during the problem framing stage. StaticsTutor was developed to analyze solutions from a student-drawn Diagram and recognize misconceptions at the earliest stages of problem framing, without requiring numerical force values or the need to provide equilibrium equations. Preliminary results with 81 undergraduates showed that it detects several frequent misconceptions in statics and that students are interested in using it, though they have suggestions for improvement. This research offers insights in the development of a Diagram-based tutor to help problem framing, which can be generalized to tutors for other forms of Diagrams.
John K. Jackman - One of the best experts on this subject based on the ideXlab platform.
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staticstutor Free Body Diagram tutor for problem framing
Intelligent Tutoring Systems, 2014Co-Authors: Enruo Guo, Stephen B. Gilbert, John K. Jackman, Gloria Starns, Matthew Hagge, Leann Faidley, Mostafa AminnaseriAbstract:While intelligent tutoring systems have been designed to teach Free-Body Diagrams, existing software often forces students to define variables and equations that may not be necessary for conceptual understanding during the problem framing stage. StaticsTutor was developed to analyze solutions from a student-drawn Diagram and recognize misconceptions at the earliest stages of problem framing, without requiring numerical force values or the need to provide equilibrium equations. Preliminary results with 81 undergraduates showed that it detects several frequent misconceptions in statics and that students are interested in using it, though they have suggestions for improvement. This research offers insights in the development of a Diagram-based tutor to help problem framing, which can be generalized to tutors for other forms of Diagrams.
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StaticsTutor: Free Body Diagram Tutor for Problem Framing
Lecture Notes in Computer Science, 2014Co-Authors: Enruo Guo, Stephen B. Gilbert, John K. Jackman, Gloria Starns, Matthew Hagge, Leann Faidley, Mostafa Amin-naseriAbstract:In this paper, we compare pioneer methods of educational data mining field with recommender systems techniques for predicting student performance. Additionally, we study the importance of including students’ attempt time sequences of parameterized exercises. The approaches we use are Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), Bayesian Probabilistic Tensor Factorization (BPTF), and Bayesian Probabilistic Matrix Factorization (BPMF). The last two approaches are from the recommender system’s field.We approach the problem using question-level Knowledge Components (KCs) and test the methods using cross-validation. In this work, we focus on predicting students’ performance in parameterized exercises. Our experiments shows that advanced recommender system techniques are as accurate as the pioneer methods in predicting student performance. Also, our studies show the importance of considering time sequence of students’ attempts to achieve the desirable accuracy
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Intelligent Tutoring Systems - StaticsTutor: Free Body Diagram Tutor for Problem Framing
Intelligent Tutoring Systems, 2014Co-Authors: Enruo Guo, Stephen B. Gilbert, John K. Jackman, Gloria Starns, Matthew Hagge, Leann Faidley, Mostafa Amin-naseriAbstract:While intelligent tutoring systems have been designed to teach Free-Body Diagrams, existing software often forces students to define variables and equations that may not be necessary for conceptual understanding during the problem framing stage. StaticsTutor was developed to analyze solutions from a student-drawn Diagram and recognize misconceptions at the earliest stages of problem framing, without requiring numerical force values or the need to provide equilibrium equations. Preliminary results with 81 undergraduates showed that it detects several frequent misconceptions in statics and that students are interested in using it, though they have suggestions for improvement. This research offers insights in the development of a Diagram-based tutor to help problem framing, which can be generalized to tutors for other forms of Diagrams.
