Relevant bibliographies by topics / Visual problem solving

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Visual problem solving'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Visual problem solving"

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Beveridge, M., and E. Parkins. "Visual representation in analogical problem solving." Memory & Cognition 15, no. 3 (1987): 230–37. http://dx.doi.org/10.3758/bf03197721.

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Campbell, K. Jennifer, Kevin F. Collis, and Jane M. Watson. "Visual processing during mathematical problem solving." Educational Studies in Mathematics 28, no. 2 (1995): 177–94. http://dx.doi.org/10.1007/bf01295792.

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Davies, Jim, Nancy J. Nersessian, and Ashok K. Goel. "Visual Models in Analogical Problem Solving." Foundations of Science 10, no. 1 (2005): 133–52. http://dx.doi.org/10.1007/s10699-005-3009-2.

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Ruliani, Iva Desi, Nizaruddin Nizaruddin, and Yanuar Hery Murtianto. "Profile Analysis of Mathematical Problem Solving Abilities with Krulik & Rudnick Stages Judging from Medium Visual Representation." JIPM (Jurnal Ilmiah Pendidikan Matematika) 7, no. 1 (2018): 22. http://dx.doi.org/10.25273/jipm.v7i1.2123.

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The ability to solve mathematical problems is very important in learning math and everyday life. According to Krulik & Rudnick there are 5 stages of problem solving that is Read, Explore, Select A Strategy, Solve And Look Back. Mathematical problems require multiple representational skills to communicate problems, one of which is visual representation. Trigonometry is one of the materials that uses visual representation. This research is a qualitative descriptive research that aims to describe the ability of problem solving mathematics with Krulik & Rudnick stages in terms of visual representation. The study was conducted in MAN 2 Brebes. Determination of Subjects in this study using Purposive Sampling. Research instruments used to obtain the required data are visual representation and problem-solving tests, and interview guidelines. The data obtained were analyzed based on the Krulik & Rudnick problem solving indicator. Subjects in this study were subjects with moderate visual representation. Based on the results, problem solving ability of the subject is not fully fulfilled. Subjects with visual representations are able to do problem solving well that is solving the problem through a concept that is understood without visualization of the image. Subjects with visual representations are having a schematic visual representation type.
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Polivanova, N. I. "Visual Image Regulation in Joint Problem-solving." Soviet Psychology 28, no. 5 (1990): 54–68. http://dx.doi.org/10.2753/rpo1061-0405280554.

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Lovett, Andrew, and Kenneth Forbus. "Modeling visual problem solving as analogical reasoning." Psychological Review 124, no. 1 (2017): 60–90. http://dx.doi.org/10.1037/rev0000039.

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GOLDSCHMIDT, GABRIELA. "SERIAL SKETCHING: VISUAL PROBLEM SOLVING IN DESIGNING." Cybernetics and Systems 23, no. 2 (1992): 191–219. http://dx.doi.org/10.1080/01969729208927457.

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Hortin, John A., Robert L. Ohlsen, and Barbara S. Newhouse. "Research for Teachers on Visual Thinking to Solve Verbal Problems." Journal of Educational Technology Systems 13, no. 4 (1985): 299–303. http://dx.doi.org/10.2190/hj8h-fyv6-2a0g-p8h2.

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If graduate students are given training in visual thinking, they will be able to use visual thinking for solving verbal problems. One hundred thirty-three graduate students participated in this study to determine whether students could be taught how to use images of the mind for problem solving. Two important activities were stressed: 1) imagery for problem solving and 2) the active participation from students. The authors believe that their study shows the importance of allowing students to use imagery in the problem solving process.
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Sholihah, Ummu, and Maryono Maryono. "Students’ visual thinking ability in solving the integral problem." JRAMathEdu (Journal of Research and Advances in Mathematics Education) 5, no. 2 (2020): 175–86. http://dx.doi.org/10.23917/jramathedu.v5i2.10286.

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Visual thinking plays an essential role in solving problems and in learning mathematics. Many students do not understand how to graphically or geometrically represent problems and solve algebra problems. Visual thinking is the ability, process, and results of creating, interpreting, using, and imagining images and diagrams on paper or with technological tools, describing and communicating information and ideas, developing ideas, and understanding improvement. This research describes students’ visual thinking ability to solve integral problems. The approach used in this study was descriptive qualitative. The subjects in this study were three students from the Department of Mathematics Education at the State Islamic Institute of Tulungagung. The data were collected by using tests and interviews. The steps to analyze the data were categorization, reduction, exposure, interpretation, and conclusion. Based on the analysis of students’ visual thinking skills in solving integral problems, there were three levels of visual thinking: semi-local visual, local visual, and global visual. At the semi-local visual level, students could only understand algebraically, and they have not shown it graphically at all. Meanwhile, at the local visual level, they have already understood geometry as an alternative language and been able graphically represented problems or concepts, even though it was not perfectly done yet. While on a global visual level, they could perfectly visualize visual thinking indicators, understand algebra and geometry as alternative languages for problem-solving, extract specific information from diagrams, graph problems, and use them to solve problems perfectly.
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Beveridge, M., and E. Parkins. "Erratum to: Visual representation in analogical problem solving." Memory & Cognition 15, no. 5 (1987): 461. http://dx.doi.org/10.3758/bf03197736.

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Dissertations / Theses on the topic "Visual problem solving"

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Madsen, Adrian M. "Studies of visual attention in physics problem solving." Diss., Kansas State University, 2013. http://hdl.handle.net/2097/15429.

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Doctor of Philosophy<br>Department of Physics<br>N. Sanjay Rebello<br>The work described here represents an effort to understand and influence visual attention while solving physics problems containing a diagram. Our visual system is guided by two types of processes -- top-down and bottom-up. The top-down processes are internal and determined by ones prior knowledge and goals. The bottom-up processes are external and determined by features of the visual stimuli such as color, and luminance contrast. When solving physics problems both top-down and bottom-up processes are active, but to varying degrees. The existence of two types of processes opens several interesting questions for physics education. For example, how do bottom-up processes influence problem solvers in physics? Can we leverage these processes to draw attention to relevant diagram areas and improve problem-solving? In this dissertation we discuss three studies that investigate these open questions and rely on eye movements as a primary data source. We assume that eye movements reflect a person’s moment-to-moment cognitive processes, providing a window into one’s thinking. In our first study, we compared the way correct and incorrect solvers viewed relevant and novice-like elements in a physics problem diagram. We found correct solvers spent more time attending to relevant areas while incorrect solvers spent more time looking at novice-like areas. In our second study, we overlaid these problems with dynamic visual cues to help students’ redirect their attention. We found that in some cases these visual cues improved problem-solving performance and influenced visual attention. To determine more precisely how the perceptual salience of diagram elements influenced solvers’ attention, we conducted a third study where we manipulated the perceptual salience of the diagram elements via changes in luminance contrast. These changes did not influence participants’ answers or visual attention. Instead, similar to our first study, the time spent looking in various areas of the diagram was related to the correctness of an answer. These results suggest that top-down processes dominate while solving physics problems. In sum, the study of visual attention and visual cueing in particular shows that attention is an important component of physics problem-solving and can potentially be leveraged to improve student performance.
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Rogers, Erika. "Visual interaction : a link between perception and problem-solving." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/9117.

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Azevedo, Roger. "Expert problem solving in mammogram interpretation, a visual cognitive task." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0002/NQ44353.pdf.

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Rouinfar, Amy. "Influence of visual cueing and outcome feedback on physics problem solving and visual attention." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/18725.

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Doctor of Philosophy<br>Department of Physics<br>N. Sanjay Rebello<br>Research has demonstrated that attentional cues overlaid on diagrams and animations can help students attend to the relevant areas and facilitate problem solving. In this study we investigate the influence of visual cues and outcome feedback on students’ problem solving, performance, reasoning, and visual attention as they solve conceptual physics problems containing a diagram. The participants (N=90) were enrolled in an algebra-based physics course and were individually interviewed. During each interview students solved four problem sets while their eye movements were recorded. The problem diagrams contained regions that were relevant to solving the problem correctly and separate regions related to common incorrect responses. Each problem set contained an initial problem, six isomorphic training problems, and a transfer problem. Those in the cued condition saw visual cues overlaid on the training problems. Those in the feedback conditions were told if their responses (answer and explanation) were correct or incorrect. Students’ verbal responses were used to determine their accuracy. The study produced two major findings. First, short duration visual cues coupled with correctness feedback can improve problem solving performance on a variety of insight physics problems, including transfer problems not sharing the surface features of the training problems, but instead sharing the underlying solution path. Thus, visual cues can facilitate re-representing a problem and overcoming impasse, enabling a correct solution. Importantly, these cueing effects on problem solving did not involve the solvers’ attention necessarily embodying the solution to the problem. Instead, the cueing effects were caused by solvers attending to and integrating relevant information in the problems into a solution path. Second, these short duration visual cues when administered repeatedly over multiple training problems resulted in participants becoming more efficient at extracting the relevant information on the transfer problem, showing that such cues can improve the automaticity with which solvers extract relevant information from a problem. Both of these results converge on the conclusion that lower-order visual processes driven by attentional cues can influence higher-order cognitive processes associated with problem solving.
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Krawec, Jennifer Lee. "Problem Representation and Mathematical Problem Solving of Students of Varying Math Ability." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/455.

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The purpose of this study was to examine differences in math problem solving among students with learning disabilities (LD), low-achieving (LA) students, and average-achieving (AA) students. The primary interest was to analyze the problem representation processes students use to translate and integrate problem information as they solve math word problems. Problem representation processes were operationalized as (a) paraphrasing the problem and (b) visually representing the problem. Paraphrasing accuracy (i.e., paraphrasing relevant information, paraphrasing irrelevant linguistic information, and paraphrasing irrelevant numerical information), visual representation accuracy (i.e., visual representation of relevant information, visual representation of irrelevant linguistic information, and visual representation of irrelevant numerical information), and problem-solving accuracy were measured in eighth-grade students with LD (n = 25), LA students (n = 30), and AA students (n = 29) using a researcher-modified version of the Mathematical Processing Instrument (MPI). Results indicated that problem-solving accuracy was significantly and positively correlated to relevant information in both the paraphrasing and the visual representation phases and significantly negatively correlated to linguistic and numerical irrelevant information for the two constructs. When separated by ability, students with LD showed a different profile as compared to the LA and AA students with respect to the relationships among the problem-solving variables. Mean differences showed that students with LD differed significantly from LA students in that they paraphrased less relevant information and also visually represented less irrelevant numerical information. Paraphrasing accuracy and visual representation accuracy were each shown to account for a statistically significant amount of variance in problem-solving accuracy when entered in a hierarchical model. Finally, the relationship between visual representation of relevant information and problem-solving accuracy was shown to be dependent on ability after controlling for the problem-solving variables and ability. Implications for classroom instruction for students with and without LD are discussed.
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Tweedie, Lisa Anne. "Exploiting interactivity in graphical problem-solving : from visual cues to insight." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264203.

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Davies, Jim. "Constructive Adaptive Visual Analogy." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4775.

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Visual knowledge appears to be an important part of problem solving, but the role of visual knowledge in analogical problem solving is still somewhat mysterious. In this work I present the Constructive Adaptive Visual Analogy theory, which claims that visual knowledge is helpful for solving problems analogically and suggests a mechanism for how it might be accomplished. Through evaluations using an implemented computer program, cognitive models of some of the visual aspects of experimental participants, and a psychological experiment, I support four claims: First, visual knowledge alone is sufficient for transfer of some problem solving procedures. Second, visual knowledge facilitates transfer even when non-visual knowledge might be available. Third, the successful transfer of strongly-ordered procedures in which new objects are created requires the reasoner to generate intermediate knowledge states and mappings between the intermediate knowledge states of the source and target analogs. And finally, that visual knowledge alone is insufficient for evaluation of the results of transfer.
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Wu, Xian. "Influence of multimedia hints on conceptual physics problem solving and visual attention." Diss., Kansas State University, 2016. http://hdl.handle.net/2097/32890.

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Doctor of Philosophy<br>Department of Physics<br>Brett D. DePaola<br>Nobel S. Rebello<br>Previous research has showed that visual cues can improve learners' problem solving performance on conceptual physics tasks. In this study we investigated the influence of multimedia hints that included visual, textual, and audio modalities, and all possible combinations thereof, on students' problem solving performance and visual attention. The participants (N = 162) were recruited from conceptual physics classes for this study. Each of them participated in an individual interview, which contained four task sets. Each set contained one initial task, six training tasks, one near transfer task and one far transfer task. We used a 2 (visual hint/no visual hint) x 2 (text hint/no text hint) x 2 (audio hint/no audio hint) between participant quasi-experimental design. Participants were randomly assigned into one of the eight conditions and were provided hints for training tasks, corresponding to the assigned condition. Our results showed that problem solving performance on the training tasks was affected by hint modality. Unlike what was predicted by Mayer's modality principle, we found evidence of a reverse modality effect, in which text hints helped participants solve the physics tasks better than audio hints. Then we studied students’ visual attention as they solved these physics tasks. We found the participants preferentially attended to visual hints over text hints when they were presented simultaneously. This effect was unaffected by the inclusion of audio hints. Text hints also imposed less cognitive load than audio hints, as measured by fixation durations. And presenting visual hints caused more cognitive load while fixating expert-like interest areas than during the time intervals before and after hints. A theoretical model is proposed to explain both problem solving performance and visual attention. According to the model, because visual hints integrated the functions of selection, organization, and integration, this caused a relatively heavy cognitive load yet improved problem solving performance. Furthermore, text hints were a better resource for complex linguistic information than transient audio hints. We also discuss limitations of the current study, which may have led to results contrary to Mayer's modality principle in some respects, but consistent with it in others.
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Banerjee, Bonny. "Spatial problem solving for diagrammatic reasoning." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1194455860.

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Webb, Julie Marie. "Dialogue During Team Problem Solving Using Visual Representation Boundary Objects: A Case Study." Scholarly Commons, 2019. https://scholarlycommons.pacific.edu/uop_etds/3648.

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Organizations benefit from the knowledge held by individual members as well as knowledge that is shared among those members. In order for knowledge to co-develop between members, and to spread, organizations must provide opportunities for members to collaborate. Organizational teams sometimes require assistance with interpersonal communication, establishing consensus, and sharing knowledge when collaborating. Group facilitators can offer guidance and intervene when teams need support. In addition, teams can find support through the use of visual representation boundary objects (VRBOs) to build trust, improve communication, increase cooperation, and share ideas. This study explores how knowledge is shared between team members and uncovers the importance of social interaction during the co-development of shared knowledge. The role that group facilitators play in team collaboration is highlighted. The results of the study indicate that a positive relationship exists between the use of a VRBO and the development of shared knowledge amongst a team. Patterns emerged from the findings that reveal a structure to the team’s collective meaning making that constitutes an underlying theory of action. The author examines the benefits of using VRBOs for teams and organizations including improved collaboration and communication.
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Books on the topic "Visual problem solving"

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The designer's eye: Visual problem-solving in architecture. W.W. Norton, 2002.

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Stone, R. J. Multilink fraction activities 2: Problem solving. E.J. Arnold, 1990.

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Problem solving with polyhedra dice. Cuisenaire Co. of America, 1994.

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Richard, Wilde, ed. Visual literacy: A conceptual approach to graphic problem solving. Watson-Guptill, 1991.

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The diagrams book: 50 ways to solve any problem visually. LID, 2013.

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Logical problem solving before the flowchart with C++ and Visual Basic applications. Prentice Hall, 2002.

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Dale, Nell B. Programming and problem solving with C++. 3rd ed. Jones and Bartlett Publishers, 2002.

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Chip, Weems, and Headington Mark R, eds. Programming and problem solving with C++. 2nd ed. Jones and Bartlett, 2000.

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Chip, Weems, and Headington Mark R, eds. Programming and problem solving with C++. D.C. Heath, 1996.

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Chip, Weems, ed. Programming and problem solving with C++. 4th ed. Jones and Bartlett Publishers, 2005.

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Book chapters on the topic "Visual problem solving"

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Davies, Jim, Ashok K. Goel, and Nancy J. Nersessian. "Transfer in Visual Case-Based Problem Solving." In Case-Based Reasoning Research and Development. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11536406_15.

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Bennett, Kevin B., John M. Flach, Timothy R. McEwen, and Olivia Fox. "Enhancing creative problem solving through visual display design." In APA handbook of human systems integration. American Psychological Association, 2015. http://dx.doi.org/10.1037/14528-026.

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Zhu, Ying, Xiaoyuan Suo, and G. Scott Owen. "A Visual Data Exploration Framework for Complex Problem Solving Based on Extended Cognitive Fit Theory." In Advances in Visual Computing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10520-3_83.

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Giurfa, Martin. "Visual Cognition in Honey Bees: From Elemental Visual Learning to Non-elemental Problem Solving." In Honeybee Neurobiology and Behavior. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2099-2_35.

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Sathyajit, B. P., and C. Shunmuga Velayutham. "Visual Analysis of Genetic Algorithms While Solving 0-1 Knapsack Problem." In Computational Vision and Bio Inspired Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71767-8_6.

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Abdelrahman, Mostafa, Asem Ali, Shireen Elhabian, and Aly A. Farag. "Solving Geometric Co-registration Problem of Multi-spectral Remote Sensing Imagery Using SIFT-Based Features toward Precise Change Detection." In Advances in Visual Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24031-7_61.

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Zaman, Halimah Badioze, Azlina Ahmad, Aliimran Nordin, et al. "Computational Thinking (CT) Problem Solving Orientation Based on Logic-Decomposition-Abstraction (LDA) by Rural Elementary School Children Using Visual-Based Presentations." In Advances in Visual Informatics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34032-2_64.

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Giurfa, Martin. "Visual learning in social insects: From simple associations to higher-order problem solving." In Sensory Perception. Springer Vienna, 2012. http://dx.doi.org/10.1007/978-3-211-99751-2_7.

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Aufderheide, Dominik, Werner Krybus, Ulf Witkowski, and Gerard Edwards. "Solving the PnP Problem for Visual Odometry – An Evaluation of Methodologies for Mobile Robots." In Advances in Autonomous Robotics. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32527-4_54.

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Zhang, Qiuju, Menno-Jan Kraak, and Connie A. Blok. "Structuring Relations between User Tasks and Interactive Tasks Using a Visual Problem-Solving Approach." In Lecture Notes in Geoinformation and Cartography. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-19602-2_7.

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Conference papers on the topic "Visual problem solving"

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Souls, Kevin. "Advanced problem solving." In ACM SIGGRAPH 97 Visual Proceedings: The art and interdisciplinary programs of SIGGRAPH '97. ACM Press, 1997. http://dx.doi.org/10.1145/259081.259320.

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Undreiu, Lucian, David Schuster, Adriana Undreiu, Charles Henderson, Mel Sabella, and Leon Hsu. "Interactive Problem Solving Tutorials Through Visual Programming." In 2008 PHYSICS EDUCATION RESEARCH CONFERENCE. AIP, 2008. http://dx.doi.org/10.1063/1.3021258.

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Thompson, Robert H. "Problem formulation affordances for computer supported collaborative problem solving." In 2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, 2015. http://dx.doi.org/10.1109/vlhcc.2015.7357231.

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Fan, Sandra B. "Roles in Online Collaborative Problem Solving." In 2010 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, 2010. http://dx.doi.org/10.1109/vlhcc.2010.51.

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Rogers, Erika, Robin R. Murphy, and Barb Ericson. "Agent-based expert assistance for visual problem solving." In the first international conference. ACM Press, 1997. http://dx.doi.org/10.1145/267658.267690.

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Jones, Benjamin T. "Human-AI Interaction in Symbolic Problem Solving." In 2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, 2018. http://dx.doi.org/10.1109/vlhcc.2018.8506542.

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Li, Guohui. "A model of visual attention for locating region of interest in large background." In 2011 International Conference on Computational Problem-Solving (ICCP). IEEE, 2011. http://dx.doi.org/10.1109/iccps.2011.6092281.

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Juan Qin, Yutang Ye, Juanxiu Liu, et al. "A new method of signal processing of photoelectric encoder in visual optical robot with multi-phalanges." In 2012 International Conference on Computational Problem-Solving (ICCP). IEEE, 2012. http://dx.doi.org/10.1109/iccps.2012.6384269.

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Kostousov, Sergei A., and Irina V. Simonova. "VISUAL MODELING FOR EXPLORATORY PROBLEM SOLVING ON COMPUTER SCIENCE LESSONS." In International Conference Cognition and Exploratory Learning in Digital Age 2019. IADIS Press, 2019. http://dx.doi.org/10.33965/celda2019_201911l033.

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Kasparova, Angelika, Oya Celiktutan, and Mutlu Cukurova. "Inferring Student Engagement in Collaborative Problem Solving from Visual Cues." In ICMI '20: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. ACM, 2020. http://dx.doi.org/10.1145/3395035.3425961.

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