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Academic literature on the topic 'Jigsaw puzzles'

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Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Jigsaw puzzles"

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Verdine, Brian N., Georgene L. Troseth, Robert M. Hodapp, and Elisabeth M. Dykens. "Strategies and Correlates of Jigsaw Puzzle and Visuospatial Performance by Persons With Prader-Willi Syndrome." American Journal on Mental Retardation 113, no. 5 (September 1, 2008): 343–55. http://dx.doi.org/10.1352/2008.113:342-355.

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Abstract Some individuals with Prader-Willi syndrome exhibit strengths in solving jigsaw puzzles. We compared visuospatial ability and jigsaw puzzle performance and strategies of 26 persons with Prader-Willi syndrome and 26 MA-matched typically developing controls. Individuals with Prader-Willi syndrome relied on piece shape. Those in the control group used a different, picture-focused strategy. Individuals with Prader-Willi syndrome performed better than did the control group on an achromatic interlocking puzzle, whereas scores on puzzles with pictures (interlocking or noninterlocking) did not differ. Visuospatial scores related to performance on all puzzles in the control group and on the noninterlocking puzzle in the Prader-Willi syndrome group. The most proficient jigsaw puzzlers with Prader-Willi syndrome tended to be older and have shape-based strategies.
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Gebers, Jane. "Jigsaw Puzzles." Academic Therapy 20, no. 5 (May 1985): 548–49. http://dx.doi.org/10.1177/105345128502000506.

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Visan, Ioana. "Inflammasome jigsaw puzzles." Nature Immunology 13, no. 6 (May 18, 2012): 533. http://dx.doi.org/10.1038/ni.2327.

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Isaacs, Carol, and Julie Fisher. "Sharing Teaching Ideas: Puzzles, Puzzles,…" Mathematics Teacher 85, no. 4 (April 1992): 278–79. http://dx.doi.org/10.5951/mt.85.4.0278.

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… and more puzzles. Laura is trying to fit tangram pieces into the outline on a worksheet. Sergei is maneuvering a marble through a wooden maze while Brad is manipulating a ball using two poles in a game called “shoot the integer,” in which students try to score the highest number possible. Jessica is working on a jigsaw puzzle that has a plus sign on one side and a minus sign on the other. A classroom visitor would think that Fairfax County spends all its money on puzzles! Actually, all these puzzles were made by our students.
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Million, Alison. "Saved by Dissections. The Popularity of Jigsaw Puzzles in Times of Calm and of Crisis. Are Librarians Dissectologists and What Might We Learn from the Bigger Picture?" Legal Information Management 20, no. 3 (September 2020): 143–50. http://dx.doi.org/10.1017/s1472669620000341.

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AbstractIn the 1760s a newly qualified apprentice to the King's Geographer hit upon the idea of cutting up maps for children to assemble as a geographical teaching aid. Dissected maps remain popular to this day in their evolved form as jigsaw puzzles. This article, written by Alison Million during the Covid-19 lockdown when jigsaws have exploded in popularity, looks at their history and at research projects which have established their cognitive benefits or have used them as an inexpensive non-digital tool. By considering papers written on librarians’ thinking styles and on personality it seeks to establish with the help of a short survey whether parallels might exist between the cognitive skillsets of the jigsaw puzzler and those of the librarian.
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Ma, Chang-Hsian, Chien-Liang Lu, and Huang-Chia Shih. "Vision-Based Jigsaw Puzzle Solving with a Robotic Arm." Sensors 23, no. 15 (August 3, 2023): 6913. http://dx.doi.org/10.3390/s23156913.

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This study proposed two algorithms for reconstructing jigsaw puzzles by using a color compatibility feature. Two realistic application cases were examined: one involved using the original image, while the other did not. We also calculated the transformation matrix to obtain the real positions of each puzzle piece and transmitted the positional information to the robotic arm, which then put each puzzle piece in its correct position. The algorithms were tested on 35-piece and 70-piece puzzles, achieving an average success rate of 87.1%. Compared with the human visual system, the proposed methods demonstrated enhanced accuracy when handling more complex textural images.
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Stewart, Ian. "Two-Way Jigsaw Puzzles." Scientific American 277, no. 4 (October 1997): 140–45. http://dx.doi.org/10.1038/scientificamerican1097-140.

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Brown, Burnell R. "Shibboleths and Jigsaw Puzzles." Anesthesiology 82, no. 3 (March 1, 1995): 607–8. http://dx.doi.org/10.1097/00000542-199503000-00001.

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Song, Xingke, Jiahuan Jin, Chenglin Yao, Shihe Wang, Jianfeng Ren, and Ruibin Bai. "Siamese-Discriminant Deep Reinforcement Learning for Solving Jigsaw Puzzles with Large Eroded Gaps." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 2303–11. http://dx.doi.org/10.1609/aaai.v37i2.25325.

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Jigsaw puzzle solving has recently become an emerging research area. The developed techniques have been widely used in applications beyond puzzle solving. This paper focuses on solving Jigsaw Puzzles with Large Eroded Gaps (JPwLEG). We formulate the puzzle reassembly as a combinatorial optimization problem and propose a Siamese-Discriminant Deep Reinforcement Learning (SD2RL) to solve it. A Deep Q-network (DQN) is designed to visually understand the puzzles, which consists of two sets of Siamese Discriminant Networks, one set to perceive the pairwise relations between vertical neighbors and another set for horizontal neighbors. The proposed DQN considers not only the evidence from the incumbent fragment but also the support from its four neighbors. The DQN is trained using replay experience with carefully designed rewards to guide the search for a sequence of fragment swaps to reach the correct puzzle solution. Two JPwLEG datasets are constructed to evaluate the proposed method, and the experimental results show that the proposed SD2RL significantly outperforms state-of-the-art methods.
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Grim, Anna, Timothy O’Connor, Peter J. Olver, Chehrzad Shakiban, Ryan Slechta, and Robert Thompson. "Automatic Reassembly of Three-Dimensional Jigsaw Puzzles." International Journal of Image and Graphics 16, no. 02 (April 2016): 1650009. http://dx.doi.org/10.1142/s0219467816500091.

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In this paper, we present an effective algorithm for reassembling three-dimensional apictorial jigsaw puzzles obtained by dividing a curved surface into a finite number of interlocking pieces. As such, our algorithm does not make use of any picture or design that may be painted on the surface; nor does it require a priori knowledge of the overall shape of the original surface. A motivating example is the problem of virtually reconstructing a broken ostrich egg shell. In order to develop and test the algorithm, we also devise a method for constructing synthetic three-dimensional puzzles by randomly distributing points on a compact surface with respect to surface area measure, then determining the induced Voronoi tessellation, and finally curving the Voronoi edges by using Bezier curves with selected control points. Our edge-matching algorithm relies on the method of Euclidean signature curves. The edges of the puzzle pieces are divided into bivertex arcs, whose signatures are directly compared. The algorithm has been programmed in Matlab and is able to successfully reassemble a broad range of artificial puzzles, including those subjected to a reasonable amount of noise. Moreover, significant progress has been made on reassembly of the real-world ostrich egg data.
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Dissertations / Theses on the topic "Jigsaw puzzles"

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Tybon, Robert, and n/a. "Generating Solutions to the Jigsaw Puzzle Problem." Griffith University. School of Management, 2004. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20041101.085937.

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This thesis examines the problem of the automated re-assembly of jigsaw puzzles. The objectives of this research are as follows: to provide a clear statement of the jigsaw puzzle re-assembly problem; to find out which solution technique is best suited to this problem; to determine the level of sensitivity of the proposed solution technique when solving different variations of this problem; and to explore solution methods for solving incomplete jigsaw puzzles (puzzles with missing pieces). The jigsaw puzzle re-assembly problem has been investigated only intermittently in the research literature. This work presents an extensive examination of the suitability and efficiency of the standard solution techniques that can be applied to this problem. A detailed comparison between different solution methods including Genetic Algorithms, Simulated Annealing, Tabu Search and Constraint Satisfaction Programming, shows that a constraint-based approach is the most efficient method of generating solutions to the jigsaw puzzle problem. The proposed re-assembly algorithm is successful. Consequently, it can be used in development of automated solution generators for other problems in the same domain, thus creating new theoretical and applied directions in this field of research. One potential theoretical line of research concerns jigsaw puzzles that do not have a complete set of puzzle pieces. These incomplete puzzles represent a difficult aspect of this problem that is outlined but can not be resolved in the current research. The computational experiments conducted in this thesis demonstrate that the proposed algorithm being optimised to re-assemble the jigsaw puzzles is not efficient when applied to the puzzles with missing pieces. Further work was undertaken to modify the proposed algorithm to enable efficient re-assembly of incomplete jigsaw puzzles. Consequently, an original heuristic strategy, termed Empty Slot Prediction, was developed to support the proposed algorithm, and proved successful when applied to certain sub-classes of this problem. The results obtained indicate that no one algorithm can be used to solve the multitude of possible scenarios involved in the re-assembly of incomplete jigsaw puzzles. Other variations of the jigsaw puzzle problem that still remain unsolved are presented as avenues for future research. The solution of this problem involves a number of procedures with significant applications in other computer-related areas such as pattern recognition, feature and shape description, boundary-matching, and heuristic modelling. It also has more practical applications in robotic vision and reconstruction of broken artefacts in archaeology.
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Tybon, Robert. "Generating Solutions to the Jigsaw Puzzle Problem." Thesis, Griffith University, 2004. http://hdl.handle.net/10072/366062.

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This thesis examines the problem of the automated re-assembly of jigsaw puzzles. The objectives of this research are as follows: to provide a clear statement of the jigsaw puzzle re-assembly problem; to find out which solution technique is best suited to this problem; to determine the level of sensitivity of the proposed solution technique when solving different variations of this problem; and to explore solution methods for solving incomplete jigsaw puzzles (puzzles with missing pieces). The jigsaw puzzle re-assembly problem has been investigated only intermittently in the research literature. This work presents an extensive examination of the suitability and efficiency of the standard solution techniques that can be applied to this problem. A detailed comparison between different solution methods including Genetic Algorithms, Simulated Annealing, Tabu Search and Constraint Satisfaction Programming, shows that a constraint-based approach is the most efficient method of generating solutions to the jigsaw puzzle problem. The proposed re-assembly algorithm is successful. Consequently, it can be used in development of automated solution generators for other problems in the same domain, thus creating new theoretical and applied directions in this field of research. One potential theoretical line of research concerns jigsaw puzzles that do not have a complete set of puzzle pieces. These incomplete puzzles represent a difficult aspect of this problem that is outlined but can not be resolved in the current research. The computational experiments conducted in this thesis demonstrate that the proposed algorithm being optimised to re-assemble the jigsaw puzzles is not efficient when applied to the puzzles with missing pieces. Further work was undertaken to modify the proposed algorithm to enable efficient re-assembly of incomplete jigsaw puzzles. Consequently, an original heuristic strategy, termed Empty Slot Prediction, was developed to support the proposed algorithm, and proved successful when applied to certain sub-classes of this problem. The results obtained indicate that no one algorithm can be used to solve the multitude of possible scenarios involved in the re-assembly of incomplete jigsaw puzzles. Other variations of the jigsaw puzzle problem that still remain unsolved are presented as avenues for future research. The solution of this problem involves a number of procedures with significant applications in other computer-related areas such as pattern recognition, feature and shape description, boundary-matching, and heuristic modelling. It also has more practical applications in robotic vision and reconstruction of broken artefacts in archaeology.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Management
Faculty of Commerce and Management
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Paumard, Marie-Morgane. "Résolution automatique de puzzles par apprentissage profond." Thesis, CY Cergy Paris Université, 2020. http://www.theses.fr/2020CYUN1067.

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L’objectif de cette thèse est de développer des méthodes sémantiques de réassemblage dans le cadre compliqué des collections patrimoniales, où certains blocs sont érodés ou manquants.Le remontage de vestiges archéologiques est une tâche importante pour les sciences du patrimoine : il permet d’améliorer la compréhension et la conservation des vestiges et artefacts anciens. Certains ensembles de fragments ne peuvent être réassemblés grâce aux techniques utilisant les informations de contour et les continuités visuelles. Il est alors nécessaire d’extraire les informations sémantiques des fragments et de les interpréter. Ces tâches peuvent être accomplies automatiquement grâce aux techniques d’apprentissage profond couplées à un solveur, c’est-à-dire un algorithme de prise de décision sous contraintes.Cette thèse propose deux méthodes de réassemblage sémantique pour fragments 2D avec érosion, ainsi qu’un jeu de données et des métriques d’évaluation.La première méthode, Deepzzle, propose un réseau de neurones auquel succède un solveur. Le réseau de neurones est composé de deux réseaux convolutionnels siamois entraînés à prédire la position relative de deux fragments : il s'agit d'une classification à 9 classes. Le solveur utilise l’algorithme de Dijkstra pour maximiser la probabilité jointe. Deepzzle peut résoudre le cas de fragments manquants et surnuméraires, est capable de traiter une quinzaine de fragments par puzzle, et présente des performances supérieures à l’état de l’art de 25%.La deuxième méthode, Alphazzle, s’inspire d’AlphaZero et de recherche arborescente Monte Carlo (MCTS) à un joueur. Il s’agit d’une méthode itérative d’apprentissage profond par renforcement : à chaque étape, on place un fragment sur le réassemblage en cours. Deux réseaux de neurones guident le MCTS : un prédicteur d’action, qui utilise le fragment et le réassemblage en cours pour proposer une stratégie, et un évaluateur, qui est entraîné à prédire la qualité du résultat futur à partir du réassemblage en cours. Alphazzle prend en compte les relations entre tous les fragments et s’adapte à des puzzles de taille supérieure à ceux résolus par Deepzzle. Par ailleurs, Alphazzle se place dans le cadre patrimonial : en fin de réassemblage, le MCTS n’accède pas à la récompense, contrairement à AlphaZero. En effet, la récompense, qui indique si un puzzle est bien résolu ou non, ne peut être qu’estimée par l’algorithme, car seul un conservateur peut être certain de la qualité d’un réassemblage
The objective of this thesis is to develop semantic methods of reassembly in the complicated framework of heritage collections, where some blocks are eroded or missing.The reassembly of archaeological remains is an important task for heritage sciences: it allows to improve the understanding and conservation of ancient vestiges and artifacts. However, some sets of fragments cannot be reassembled with techniques using contour information or visual continuities. It is then necessary to extract semantic information from the fragments and to interpret them. These tasks can be performed automatically thanks to deep learning techniques coupled with a solver, i.e., a constrained decision making algorithm.This thesis proposes two semantic reassembly methods for 2D fragments with erosion and a new dataset and evaluation metrics.The first method, Deepzzle, proposes a neural network followed by a solver. The neural network is composed of two Siamese convolutional networks trained to predict the relative position of two fragments: it is a 9-class classification. The solver uses Dijkstra's algorithm to maximize the joint probability. Deepzzle can address the case of missing and supernumerary fragments, is capable of processing about 15 fragments per puzzle, and has a performance that is 25% better than the state of the art.The second method, Alphazzle, is based on AlphaZero and single-player Monte Carlo Tree Search (MCTS). It is an iterative method that uses deep reinforcement learning: at each step, a fragment is placed on the current reassembly. Two neural networks guide MCTS: an action predictor, which uses the fragment and the current reassembly to propose a strategy, and an evaluator, which is trained to predict the quality of the future result from the current reassembly. Alphazzle takes into account the relationships between all fragments and adapts to puzzles larger than those solved by Deepzzle. Moreover, Alphazzle is compatible with constraints imposed by a heritage framework: at the end of reassembly, MCTS does not access the reward, unlike AlphaZero. Indeed, the reward, which indicates if a puzzle is well solved or not, can only be estimated by the algorithm, because only a conservator can be sure of the quality of a reassembly
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Noursobhi, Soroush. "Puzzle Up : Android Jigsaw Puzzle Game." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13460.

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Development of a jigsaw based puzzle game for Android called PuzzleUp. The idea is that user takes a picture in real time and breaks it into jigsaw pieces that has to be put together. It also features a multiplayer mode on local area network (LAN).The main programming language used is Java and the main development environment is Android Studio (Based on JetBrains Idea) and AllJoyn has been used for inter-device communication to ensure maximum compatibility.
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Yang, Xingwei. "Shape Based Object Detection and Recognition in Silhouettes and Real Images." Diss., Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/111091.

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Computer and Information Science
Ph.D.
Shape is very essential for detecting and recognizing objects. It is robust to illumination, color changes. Human can recognize objects just based on shapes, thus shape based object detection and recognition methods have been popular in many years. Due to problem of segmentation, some researchers have worked on silhouettes instead of real images. The main problem in this area is object recognition and the difficulty is to handle shapes articulation and distortion. Previous methods mainly focus on one to one shape similarity measurement, which ignores context information between shapes. Instead, we utilize graph-transduction methods to reveal the intrinsic relation between shapes on 'shape manifold'. Our methods consider the context information in the dataset, which improves the performance a lot. To better describe the manifold structure, we also propose a novel method to add synthetic data points for densifying data manifold. The experimental results have shown the advantage of the algorithm. Moreover, a novel diffusion process on Tensor Product Graph is carried out for learning better affinities between data. This is also used for shape retrieval, which reaches the best ever results on MPEG-7 dataset. As shapes are important and helpful for object detection and recognition in real images, a lot of methods have used shapes to detect and recognize objects. There are two important parts for shape based methods, model construction and object detection, recognition. Most of the current methods are based on hand selected models, which is helpful but not extendable. To solve this problem, we propose to construct model by shape matching between some silhouettes and one hand decomposed silhouette. This weakly supervised method can be used not only learn the models in one object class, but also transfer the structure knowledge to other classes, which has the similar structure with the hand decomposed silhouette. The other problem is detecting and recognizing objects. A lot of methods search the images by sliding window to detect objects, which can find the global solution but with high complexity. Instead, we use sampling methods to reduce the complexity. The method we utilized is particle filter, which is popular in robot mapping and localization. We modified the standard particle filter to make it suitable for static observations and it is very helpful for object detection. Moreover, The usage of particle filter is extended for solving the jigsaw puzzle problem, where puzzle pieces are square image patches. The proposed method is able to reach much better results than the method with Loopy Belief Propagation.
Temple University--Theses
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Bárnet, Lukáš. "Spojování nepřekrývajících se obrazů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219685.

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The Diploma thesis is concerned with coupling of images. In the first part theoretical bases necessary for successful fulfilling of the assignment are described. The second part deals with the procedures that lead to composition of the jigsaw puzzle. The last part concentrates on controlling the program. The aim of the thesis is to propound an algorithm for solving a puzzle.
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O'Brien, Rachel. "Putting together the jigsaw puzzle : women's sense of self following an episode of postpartum psychosis." Thesis, University of Surrey, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540729.

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Salter, B. W. Jim. "A jigsaw puzzle : assessing the English vocabulary level of junior secondary students in Hong Kong /." Thesis, Hong Kong : University of Hong Kong, 2002. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25262750.

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Stanczak, Arnaud. "La méthode de la "classe puzzle" est-elle efficace pour améliorer l'apprentissage ?" Thesis, Université Clermont Auvergne‎ (2017-2020), 2020. http://www.theses.fr/2020CLFAL013.

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Ce travail de recherche a pour objectif de tester les effets de la classe puzzle, ou « Jigsaw classroom », sur les apprentissages. La méthode Jigsaw est une pédagogie coopérative créée par Aronson et collaborateurs dans les années 1970, afin de favoriser l’inclusion des minorités ethniques (e.g, Mexicain·es et Afro-Américain·es) dans les écoles nouvellement désegréguées. Selon la théorie de l’interdépendance sociale les effets positifs de l’apprentissage coopératif dépendent de la structuration des interactions entre individus (Deutsch, 1949 ; Johnson & Johnson, 1989). Dans Jigsaw, la structuration de cette interdépendance provient essentiellement de la distribution de ressources complémentaires : chaque individu dispose d’une « pièce du puzzle » à reconstituer à l’aide des autres membres du groupe. La coordination des efforts entre membres devrait amener ces dernier‧es à mettre en place des interactions facilitatrices (e.g., comportements d’entraide, explications et questionnements) et aboutir à un meilleur apprentissage. Toutefois, bien que cette méthode soit présentée par ses concepteur·rices comme un outil efficace pour améliorer l’apprentissage des élèves, les preuves empiriques tendent à manquer. Dans cette thèse, l’efficacité de Jigsaw sera questionnée à travers une analyse de la littérature scientifique, ainsi qu’une méta-analyse sur les travaux récents et un ensemble d’études expérimentales menées auprès d’élèves de sixième. À notre connaissance, bien que certaines recherches testant les effets de Jigsaw soient compilées dans des méta-analyses (Kyndt et al., 2013), il n’existe pas à ce jour de méta-analyses testant spécifiquement les effets de Jigsaw sur les apprentissages. À travers six chapitres, nous tenterons d’apporter des éléments permettant d’évaluer l’efficacité de la méthode Jigsaw sur les apprentissages. Dans le chapitre 1, nous présentons la théorie de l’interdépendance sociale, plusieurs définitions et manières de structurer de la coopération entre élèves ainsi qu’une revue des leurs effets sur les apprentissages. Nous développons l’idée qu’il existe des différences d’efficacité entre les pédagogies coopératives créées entre les années 1960 et 2000 (Newmann & Thompson, 1987 ; Johnson et al., 2000), et que certaines d’entre elles n’ont pas encore fait l’objet d’une validation empirique solide. Le chapitre 2 examine l’une d’elle en détail : Jigsaw (Aronson et al., 1978 ; Aronson & Patnoe, 2011). Nous y décrivons l’évolution des études empiriques menées depuis sa création jusqu’à ce jour. Le chapitre 3 pointe certaines limites de cette littérature, notamment par rapport à la puissance statistique et les procédures méthodologiques, ainsi que les impacts qu’elles peuvent avoir sur l’estimation de l’efficacité de Jigsaw sur les apprentissages. Nous y développons aussi notre hypothèse de recherche, son opérationnalisation ainsi que les outils et procédures statistiques que nous utilisons dans les chapitres empiriques : tests d’équivalence (Lakens, 2017), plus petite taille d’effet d’intérêt (Hattie, 2009) et méta-analyses (Borenstein et al., 2010 ; Goh et al., 2016). Le chapitre 4 présente les résultats d’une méta-analyse des effets de Jigsaw sur les apprentissages, à travers des articles empiriques publiés entre les années 2000 et 2020. Nous testons plusieurs modérateurs (e.g., niveau scolaire, discipline étudiée, type de Jigsaw, localisation des recherches) afin de quantifier la dispersion des effets de Jigsaw et de mieux comprendre l’hétérogénéité entre les études. Le chapitre 5 synthétise cinq études menées auprès de populations de collégien·nes français·es dans lesquelles nous testons l’efficacité de Jigsaw sur les apprentissages comparativement à des conditions de travail individuelles (études 1 et 2), ou d’enseignement habituel avec des enseignant‧es volontaires (études 3A, 3B et 3C). [...]
The objective of this thesis is to test the effect of the Jigsaw classroom on learning. The Jigsaw classroom is a cooperative technique created by Aronson and his colleagues in the 1970s to promote the inclusion of ethnic minorities (e.g., Mexican and African-American) in desegregated schools. Although this method is presented by its developers as an effective tool for improving student learning, empirical evidence is lacking. According to the social interdependence theory, the structure of interactions between individuals determine the effects of cooperative learning (Deutsch, 1949; Johnson & Johnson, 1989). In Jigsaw, this structure comes from the distribution of complementary resources: each individual owns a “jigsaw piece”, namely a piece of information which requires the coordination of efforts among members to answer a problematic. With the help of other group members, promotive interactions (e.g., helping behaviors, explanations and questioning) should emerge which results in a better learning for the members. In this thesis, Jigsaw's effectiveness will be evaluated through a review of the scientific literature, as well as a meta-analysis of recent research and a set of experimental studies conducted among french sixth graders. To our knowledge, the experimental study of Jigsaw’s effects on learning in student populations is almost non-existent in the scientific literature and even though some research testing these effects is compiled in meta-analyses (Kyndt et al., 2013), there are no meta-analyses to date that specifficaly adress the question of Jigsaw's effects on learning. Hence, the research presented in this manuscript will attempt to evaluate the effectiveness of the Jigsaw method on learning. In Chapter 1, we present “social interdependence theory” (Johnson & Johnson, 1989, 2002, 2005), several definitions and ways of structuring cooperation between students, as well as a review of their effects on learning. Chapter 2 examines one of these cooperative technique in detail: Jigsaw (Aronson et al., 1978; Aronson & Patnoe, 2011). We describe the evolution of empirical studies conducted from its conception to the present day. Chapter 3 points out some of the limitations of this literature, particularly in terms of statistical power, and the impacts it may have on the estimation of Jigsaw's effectiveness on learning. We also develop our main hypothesis, its operationalization and the statistical tools and procedures we use in the empirical chapters: equivalence tests (Lakens, 2017), smallest effect size of interest (Hattie, 2009) and meta-analyses (Borenstein et al., 2010; Goh et al., 2016). Chapter 4 presents the results of a meta-analysis of Jigsaw's effects on learning, which synthesized empirical articles published between 2000 and 2020. We test several moderators (e.g., grade level, discipline, type of Jigsaw, location of research) in order to quantify the dispersion of Jigsaw effects and to assess heterogeneity between studies. Chapter 5 compiles five studies conducted among french sixth graders in which we test the effectiveness of Jigsaw on learning, compared to an “individual” (studies 1 and 2) or a “teaching as usual’ condition (studies 3A, 3B and 3C). The results of this chapter are interpreted with regard to the meta-analysis and the debates related to the structure of Jigsaw. In the last chapter of this manuscript, we summarize the main results developed trough the theoretical and empirical chapters. The contributions and limitations of our research are developed, as well as theoretical and practical perspectives to overcome them in view of future research
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Sullivan-Vance, Karen. "A Million Piece Jigsaw Puzzle| Transition Experiences of Foster Youth Accessing Higher Education through Community College." Thesis, Portland State University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10825438.

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A college education offers people social and economic benefits, yet youth from foster care backgrounds are less likely than their peers to attain a college education, which places this already vulnerable population at higher risk for a lifetime of living on the margins of society. Foster alumni face multiple obstacles to accessing and persisting in higher education. To facilitate and support the success of this frequently overlooked population, professionals in higher education need to understand these obstacles. Little is known about the experiences of youth with foster care backgrounds as they transition into and through higher education. Although existing research has reported the academic, health, and social effects of having been in foster care, little is known about why foster alumni do not persist in higher education. This study used student-development theory, specifically Schlossberg’s transition theory, Tinto’s theory of student departure, and Bourdieu’s work on social and cultural capital to provide a conceptual framework through which to view the lived experiences of youth with foster care backgrounds. Because, for many youths with foster care backgrounds, the pathway to the baccalaureate degree is through a community college, this study examined and explored the transition experiences of foster alumni about to begin or currently enrolled at an Oregon Community College. The study explored the factors that challenge and facilitate foster alumni persistence towards the attainment of a college degree.

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Books on the topic "Jigsaw puzzles"

1

Milne, A. A. Winnie-the-Pooh: Jigsaw book with seven jigsaws. London: Egmont, 2001.

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Kern, Evan J. Making wooden jigsaw puzzles. Mechanicsburg, Pa: Stackpole Books, 1996.

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McCann, Chris. Master pieces: The art history of jigsaw puzzles. Portland, Or: Collectors Press, 1998.

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Castles Jigsaw Cubes: Jigsaw. Flame Tree Publishing, 2007.

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Jigsaw. Pippin Publishing, 2000.

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Square Jigsaw Box Set (Jigsaw Puzzle). Templar Publishing, 2007.

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Jigsaw Puzzles Splish Splash (Jigsaw Rhymes). T & N Childrens Publishing, 2000.

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Spirit of London Jigsaw: 1000-Piece Jigsaw. Pavilion Books, 2023.

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d'Errico, Camilla. Hydie : A 1,000-Piece Pop Surrealism Jigsaw Puzzle: Jigsaw Puzzles for Adults, Jigsaw Puzzles for Kids. Potter/Ten Speed/Harmony/Rodale, 2020.

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Princess Jigsaw Puzzles. Unknown, 2013.

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Book chapters on the topic "Jigsaw puzzles"

1

Brand, Michael. "No Easy Puzzles: A Hardness Result for Jigsaw Puzzles." In Lecture Notes in Computer Science, 64–73. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07890-8_6.

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Korashy, Mostafa, Islam A. T. F. Taj-Eddin, Mahmoud Elsaadany, and Shoukry I. Shams. "Solving Jigsaw Puzzles Using Variational Autoencoders." In Advances in Intelligent Systems and Computing, 708–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55190-2_57.

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Son, Kilho, James Hays, and David B. Cooper. "Solving Square Jigsaw Puzzles with Loop Constraints." In Computer Vision – ECCV 2014, 32–46. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10599-4_3.

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Zhao, Senhua, Yue-Jiao Gong, and Xiaolin Xiao. "Multi-strategy Evolutionary Computation for Automated Jigsaw Puzzles." In Neural Information Processing, 50–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63833-7_5.

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Hynek, Josef. "Sequence Matching Genetic Algorithm for Square Jigsaw Puzzles." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 317–24. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44654-6_31.

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Marcu, Stefan-Bogdan, Yanlin Mi, Venkata V. B. Yallapragada, Mark Tangney, and Sabin Tabirca. "Generating Jigsaw Puzzles and an AI Powered Solver." In Modelling and Development of Intelligent Systems, 148–60. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27034-5_10.

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Teixeira, João M. X. N., Pedro J. L. Silva, Júlia D. T. de Souza, Filipe F. Monteiro, and Veronica Teichrieb. "JigsAR: A Mixed Reality System for Supporting the Assembly of Jigsaw Puzzles." In Design, User Experience, and Usability. Design for Contemporary Interactive Environments, 572–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49760-6_41.

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Noroozi, Mehdi, and Paolo Favaro. "Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles." In Computer Vision – ECCV 2016, 69–84. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46466-4_5.

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Ren, Zhongle, Yiming Lu, Hanxiao Wang, Yu Zhang, and Biao Hou. "SAR Scene Classification Based on Self-supervised Jigsaw Puzzles." In IFIP Advances in Information and Communication Technology, 334–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14903-0_36.

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Guo, Wenjing, Wenhong Wei, Yuhui Zhang, and Anbing Fu. "A Genetic Algorithm-Based Solver for Small-Scale Jigsaw Puzzles." In Lecture Notes in Computer Science, 362–73. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53956-6_32.

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Conference papers on the topic "Jigsaw puzzles"

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Taciano, Miguel Silva, Victor Pugliese, and Fabio Augusto Faria. "SegSemPuzzle: Solving Jigsaw Puzzles with Semantic Segmentation." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/wvc.2023.27545.

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The traditional Jigsaw Puzzle is a challenging task performed by humans, mainly due to its hardness and proven to be a NP-Complete problem. Even so, recent efforts show better performance in this task using different methods involving complex computer vision and machine learning techniques. In this sense, this paper proposes new approaches based on the semantic segmentation (SS) task to solve jigsaw puzzles (visual puzzles) in reduced training scenario. To the best of our knowledge, this is the first work in the literature that uses SS for the target application. In the performed experiments, it was possible to demonstrate that SegSemPuzzle and SegSemPuzzle-G obtained excellent results when compared with other approaches existing in literature for 3 × 3 puzzle solving tasks.
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Lau, Cheryl, Yuliy Schwartzburg, Appu Shaji, Zahra Sadeghipoor, and Sabine Süsstrunk. "Creating personalized jigsaw puzzles." In the Workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2630397.2630405.

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Bridger, Dov, Dov Danon, and Ayellet Tal. "Solving Jigsaw Puzzles With Eroded Boundaries." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00358.

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Carlucci, Fabio M., Antonio D'Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi. "Domain Generalization by Solving Jigsaw Puzzles." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00233.

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Yu, Rui, Chris Russell, and Lourdes Agapito. "Solving Jigsaw Puzzles with Linear Programming." In British Machine Vision Conference 2016. British Machine Vision Association, 2016. http://dx.doi.org/10.5244/c.30.139.

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Mondal, Debajyoti, Yang Wang, and Stephane Durocher. "Robust Solvers for Square Jigsaw Puzzles." In 2013 International Conference on Computer and Robot Vision (CRV). IEEE, 2013. http://dx.doi.org/10.1109/crv.2013.54.

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Bottoni, Paolo, and Miguel Ceriani. "Linked Data Queries as Jigsaw Puzzles." In CHItaly 2015: 11th biannual Conference of the Italian SIGCHI Chapter. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808435.2808467.

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Kimoto, Kouki, Yasuyuki Murai, Hiroyuki Tsuji, and Shinji Tokumasu. "Rectilinear Jigsaw Puzzles: Theory and Algorithms." In 2006 World Automation Congress. IEEE, 2006. http://dx.doi.org/10.1109/wac.2006.375747.

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Gallagher, A. C. "Jigsaw puzzles with pieces of unknown orientation." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6247699.

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Logeswaran, Lajanugen. "Solving Jigsaw Puzzles using Paths and Cycles." In British Machine Vision Conference 2014. British Machine Vision Association, 2014. http://dx.doi.org/10.5244/c.28.117.

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Reports on the topic "Jigsaw puzzles"

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Stanley-Wall, Nicola, Amy Cameron, Erin Hardee, Andrea Davies, Alan Prescott, Paul Harrison, Ali Floyd, et al. School of Life Sciences Research Jigsaw Puzzles. University of Dundee, July 2024. http://dx.doi.org/10.20933/100001317.

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Six images showcasing the diverse and impactful research undertaken by researchers at the School of Life Sciences. Each image is available to print in two sizes: •186 x 186 mm to create a 49-piece jigsaw puzzle •496 x 366 mm to create a 500-piece jigsaw puzzle An accompanying insert provides further information on each image.
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Sullivan-Vance, Karen. A Million Piece Jigsaw Puzzle: Transition Experiences of Foster Youth Accessing Higher Education through Community College. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6310.

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