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BC:
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PER: Problem Solving I
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Location:
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HC 3023 & 3023A |
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Date:
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Monday, Aug.01 |
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Time:
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1:00PM - 2:30PM
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Presider:
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Paul Nienaber,
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Co-Presiders(s):
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None
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Equipment:
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N/A
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BC01:
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Tutorials to Facilitate Physics Problem Solving with Differentiation and Integration
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Location:
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HC 3023 & 3023A |
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Date:
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Monday, Aug.01 |
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Time:
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1:00PM - 1:10PM
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Author:
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Dehui Hu, Kansas State University
785-532-1612, dehuihu@phys.ksu.edu
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Co-Author(s):
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Joshua Von Korff, N. Sanjay Rebello
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Abstract:
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Students in introductory-level physics encounter several difficulties when solving physics problems involving differentiation and integration. Physics instructors tend to assume that students have the prerequisite mathematical skills for success in the course, however, research has shown that most students do not know how to apply mathematical tools in a physics context. Based on the knowledge of the difficulties students with the use of differentiation and integration in physics encoutered from previous studies, we are developing instructional materials aimed at facilitating students to address these difficulties in several topics in introductory physics. We have implemented these materials in group problem-solving sessions aimed at enabling students to learn the mathematical concepts of tangent lines, slope, Riemann sum, and approximation in a physics context. We present a discussion about student difficulties on those concepts and the development of our instructional materials.
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Footnotes:
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This work is supported in part by U.S. National Science Foundation grant 0816207.
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BC02:
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The Influence of Hints and Training on Student Resource Selection
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Location:
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HC 3023 & 3023A |
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Date:
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Monday, Aug.01 |
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Time:
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1:10PM - 1:20PM
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Author:
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Joshua S. Von Korff, Kansas State University
785-532-1612, vonkorff@phys.ksu.edu
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Co-Author(s):
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Dehui Hu, N. Sanjay Rebello
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Abstract:
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We consider physics problems that require students to combine their existing resources in new ways. When students do this in the context of integration and differentiation, they have many procedures, concepts, and representations to choose from. In addition, they may have varying degrees of understanding about the procedures they invent. We examine students' resource selection in problem solving situations, using an online environment to control and monitor their progress through a series of hints. Over the course of a 30-minute testing period, students work through a single problem; initially inventing their own strategies, then following our suggestions toward particular solutions. We will present results from our assessment of students' naïve understanding, as well as the impact of cues and training after a 50-minute practice session prior to the test. We will also describe students' ability to learn new ways of thinking about the problem.
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Footnotes:
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This work is supported in part by U.S. National Science Foundation grant 0816207.
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BC03:
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Do Prescribed Prompts Prime Sensemaking During Group Problem Solving? Part One
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Location:
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HC 3023 & 3023A |
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Date:
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Monday, Aug.01 |
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Time:
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1:20PM - 1:30PM
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Author:
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Mathew A. Martinuk, University of British Columbia
7788366366, martinuk@physics.ubc.ca
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Co-Author(s):
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Joss Ives
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Abstract:
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Many researchers and textbooks have promoted the use of rigid prescribed strategies for encouraging development of expert-like problem-solving behavior in novice students. The UBC Physics 100 course has been using context-rich problems with a prescribed five-step strategy since 2007. We have been analyzing audio recordings of students during group problem-solving sessions to analyze students' epistemological framing based on the implicit goal of their discussions. By treating the goal of "understanding the physics situation" as "sensemaking," we analyze the effectiveness of structured prompts intended to promote a shift to a sensemaking discussion. This talk will describe the setting and research methods, and a subsequent talk will discuss the analysis and results.
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Footnotes:
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None
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BC04:
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Investigating Sequencing Effect on Biomedical Physics Problem Solving
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Location:
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HC 3023 & 3023A |
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Date:
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Monday, Aug.01 |
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Time:
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1:30PM - 1:40PM
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Author:
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Bijaya Aryal, University of Minnesota-Rochester
5072588216, baryal@umn.edu
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Co-Author(s):
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Robert L. Dunbar
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Abstract:
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This study focused on the effect of varying the sequence of problem solving and laboratory activities on the students' ability to solve subsequent biomedical contextual physics problems. A series of laboratory and problem solving activities were designed using concrete physical situations. Following the introduction of specific physics concepts, students worked in groups to complete related laboratories and problem solving activities. The order of problem solving and laboratory activities was regularly altered throughout the semester. Subsequently, the students were asked to solve related contextual biomedical physics problems. The result of the study indicated that altering the sequence of activities had a measurable impact on students' contextual problem solving performance and strategies.
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Footnotes:
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None
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BC05:
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How to Improve Transfer from Difficult Worked Examples by Designing a 'Good Looking' Animated Solution
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Location:
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HC 3023 & 3023A |
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Date:
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Monday, Aug.01 |
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Time:
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1:40PM - 1:50PM
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Author:
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Zhongzhou Chen
The University of Illinois at Urbana–Champaign
217-721-8411, zchen22@illinois.edu
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Co-Author(s):
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Gary Gladding
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Abstract:
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It is well known that transfer from worked examples to new problems can be very hard for students. The goal of this research is to promote transfer by improving the quality of the example solution. According to our experience, elaborate verbal explanation often seems to have little, if not negative, effects on transfer. Therefore, we focus on designing a better visual representation. Based on knowledge from grounded cognition research, we designed several animated multimedia solutions for some difficult physics problems, in which the underlying logic is illustrated through visual perception. When compared to two other very similar versions of animated solutions that lack the critical perceptual elements, the designed solutions significantly improved transfer of the underlying physics principles to harder problems. Moreover, transfer is improved even when the target problem involves largely abstract logical reasoning, and little visual-spatial reasoning.
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Footnotes:
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None
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BC06:
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The Impact of Sample Size in Using IRT with FCI
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Location:
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HC 3023 & 3023A |
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Date:
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Monday, Aug.01 |
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Time:
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1:50PM - 2:00PM
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Author:
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Li Chen
School of Electronic science and engeering, Southeast University
614-292-2450, chenli.seu@163.com
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Co-Author(s):
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Jing Han, Liangyu Peng, Yan Tu, Lei Bao
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Abstract:
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Item Response Theory is a useful tool for analyzing quantitative data. The sample size will impact the uncertainty of the estimated parameters. It is then important to find out the approximate minimum sample size, with which reliable results can be calculated. In this study, we choose R (with its LTM package) to estimate the parameters with different sample sizes, which are randomly selected from the college students' FCI data collected at The Ohio State University. The total number of the data is 3139. The results show an exponential relationship between sample size and the mean difference of the results obtained with subsets of the data. When sample size is larger than 1600, the difference is tolerable for most items and the mean total difference can be controlled within 5%. This can provide useful guide for future data analysis using IRT.
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Footnotes:
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Supported in part by NIH Award RC1RR028402 and NSF Awards DUE-0633473 and DUE-1044724
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BC07:
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The Effect of Problem Format on Students' Answers
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Location:
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HC 3023 & 3023A |
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Date:
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Monday, Aug.01 |
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Time:
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2:00PM - 2:10PM
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Author:
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Mark Ellermann, Texas Tech University
806-742-3971, mark.ellermann@ttu.edu
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Co-Author(s):
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Beth Thacker, Keith West
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Abstract:
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The same problem written in multiple formats was administered as a quiz in the large introductory physics sections in both the algebra-based and calculus-based classes. The formats included multiple choice only, multiple choice and explain your reasoning, explain your reasoning only, ranking and explaining your reasoning, and a few others. We present the data.
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Footnotes:
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This project is supported by the NIH grant 5RC1GM090897-02.
Sponsored by Beth Thacker.
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BC08:
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What Students Learn When Studying Physics Practice Exam Problems
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Location:
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HC 3023 & 3023A |
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Date:
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Monday, Aug.01 |
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Time:
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2:10PM - 2:20PM
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Author:
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Witat Fakcharoenphol
University of Illinois at Urbana Champaign
217-898-4854, fakchar1@uiuc.edu
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Co-Author(s):
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Timothy J. Stelzer
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Abstract:
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We developed a web-based tool to provide students with access to old exam problems and solutions. By controlling the order in which students saw the problems, as well as their access to solutions, we obtained data about student learning by studying old exams problems. Our data suggest that in general students learn from doing old exam problems, and that having access to the problem solutions increases their learning. However, the data also suggest the depth of learning may be relatively shallow. In addition, the data show that doing old exam problems provides impor-tant formative assessment about the student's overall preparedness for the exam, and their particular areas of strength and weakness.
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Footnotes:
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None
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BC09:
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Using Problem-Solving Computer Coaches to Explore Student Decision-Making Difficulties
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Location:
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HC 3023 & 3023A |
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Date:
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Monday, Aug.01 |
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Time:
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2:20PM - 2:30PM
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Author:
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Qing Xu, University of Minnesota-Twin cities
6126259323, qxu@physics.umn.edu
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Co-Author(s):
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Ken Heller, Leon Hsu, Andrew Mason
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Abstract:
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The Physics Education Group at the University of Minnesota has been developing Internet physics coaches to help students improve their problem-solving skills in introductory physics. In this talk, we analyze keystroke data collected from students' usage of the computer programs, including the identity and timing information for all students' keystrokes and mouse clicks while using the programs, as well as derived information such as the average time spent on each module. We use the data to try to determine how students use the computer programs, where they might have the most difficulty, and details of their decision-making behavior during the problem-solving process. Other data sources such as students' written solutions will be used as a consistency check.
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Footnotes:
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None
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