A circular layout algorithm for attributed hierarchical graphs with ports

Information visualization based on graph models is a key component of support tools for many applications in science and engineering. The Visual Graph system is intended for visualization of big amounts of complex information on the basis of attributed hierarchical graph models. In this paper, a circular layout algorithm for attributed hierarchical graphs with ports and its effective implementation in the Visual Graph system are presented.

From Hand to Eye With the Devil In-Between: Which Cognitive Mechanisms Underpin the Benefit From Handwriting Training When Learning Visual Graphs?

Cognitive science has recently shown a renewed interest on the benefit from training in handwriting (HW) when learning visual graphs, given that this learning experience improves more subsequent visual graph recognition than other forms of training. However, the underlying cognitive mechanism of this HW benefit has been elusive. Building on the 50 years of research on this topic, the present work outlines a theoretical approach to study this mechanism, specifying testable hypotheses that will allow distinguishing between confronting perspectives, i.e., symbolic accounts that hold that perceptual learning and visual analysis underpin the benefit from HW training vs. embodied sensorimotor accounts that argue for motoric representations as inner part of orthographic representations acquired via HW training. From the evidence critically revisited, we concluded that symbolic accounts are parsimonious and could better explain the benefit from HW training when learning visual graphs. The future challenge will be to put at test the detailed predictions presented here, so that the devil has no longer room in this equation.

Towards plug-and-play visual graph query interfaces

Canned patterns ( i.e. , small subgraph patterns) in visual graph query interfaces (a.k.a GUI) facilitate efficient query formulation by enabling pattern-at-a-time construction mode. However, existing GUIS for querying large networks either do not expose any canned patterns or if they do then they are typically selected manually based on domain knowledge. Unfortunately, manual generation of canned patterns is not only labor intensive but may also lack diversity for supporting efficient visual formulation of a wide range of subgraph queries. In this paper, we present a novel, generic, and extensible framework called TATTOO that takes a data-driven approach to automatically select canned patterns for a GUI from large networks. Specifically, it first decomposes the underlying network into truss-infested and truss-oblivious regions. Then candidate canned patterns capturing different real-world query topologies are generated from these regions. Canned patterns based on a user-specified plug are then selected for the GUI from these candidates by maximizing coverage and diversity , and by minimizing the cognitive load of the pattern set. Experimental studies with real-world datasets demonstrate the benefits of TATTOO. Importantly, this work takes a concrete step towards realizing plug-and-play visual graph query interfaces for large networks.

From hand to eye: A meta-analysis of the benefit from handwriting training in visual graph recognition

Handwriting (HW) training seems to boost recognition of visual graphs and learning to read more than other learning experiences. However, effects across studies appear to be variable and the underlying cognitive mechanism has been elusive. We thus conducted a meta-analysis on 50 independent experiments (with 1525 participants) to determine the magnitude of this HW benefit in visual graph recognition, while enlightening the underlying cognitive mechanism, by investigating four types of moderators: training program (type of control training, presence/absence of phonological training, and HW tasks adopted); set size and training regime (duration and frequency of training session and total amount of training); granularity of visual discrimination and perceptual learning tasks; and age of participants. The benefit from HW training was moderate-to-large and significant (Hedge’s g = 0.58, SE = .09) and was also modulated by type of control training (larger relative to motor, g = 0.78, than to visual control, g = 0.37), phonological training (larger when it was absent, g = 0.79, than present, g = 0.47), and granularity of visual discrimination (larger for fine-grained, g = 0.93, than coarse-grained, g = 0.19). These results are consistent with symbolic accounts that hold that the advantage from HW training in visual graph recognition is about perceptual learning rather than the motor act. Multiple meta-regressions also revealed that training regime modulated the HW benefit. We conclude that HW training is effective to improve visual graph recognition, and hence, is still relevant for literacy instruction in the present digital era.

Keterampilan Pengucapan Kata Melalui Model Pembelajaran Cooperative Integrated Reading and Composition bagi Anak Berkesulitan Belajar

This research was motivated by the problems found by the researcher where the researcher found a child who had difficulty learning. This problem was found in SD N 34 Siguntur Tua, the child was 12 years old but was not yet fluent in reading. This study aims to prove the effect of the CIRC learning model in improving word pronunciation skills for children with learning difficulties in grade IV at SD N 34 Siguntur Tua. This type of research is experimental in the form of Single Subject Research with A-B-A design, the research subject of a child has difficulty learning with the condition that the child often adds letters and replaces letters in reading. Data collection techniques in this study using tests. The test technique is done by assigning children to read words correctly after being given intervention with the CIRC learning model. Data were analyzed using visual graph techniques. From the research that has been done, it is found that the CIRC learning model can improve the pronunciation skills of children having difficulty learning in grade IV at SD N 34 Siguntur Tua in accordance with the results of data analysis in conditions and between conditions.

Efektivitas Bermain Lotto untuk Meningkatkan Konsentrasi Belajar bagi anak ADHD

This research is based on getting an ADHD child who has difficulty learning concentration, learning concentration in focusing on the material being worked on. This study aims to increase learning concentration for ADHD children at SDN 11 Pauh Padang. This type of research experiments in the form of a single subject (Single Subject Research) with de s ain ABA, the research subject is a child with ADHD, show symptoms of a lack of ability to concentrate full attention to the object. Data collection techniques in this study used a duration to see how long the child was able to focus on the material being worked on within 20 minutes of the learning process. Data analysis techniques using visual graph techniques. The results of this study indicate that playing lotto can increase learning concentration for ADHD children at SDN 11 Pauh Padang seen from the results of data analysis in conditions and between conditions.