Grapheme-phoneme learning in an unknown orthography: a study in typical reading and dyslexic children

In this study, we examined the learning of new grapheme-phoneme correspondences in individuals with and without dyslexia. Additionally, we investigated the relation between grapheme-phoneme learning and measures of phonological awareness, orthographic knowledge and rapid automatized naming, with a focus on the unique joint variance of grapheme-phoneme learning to word and non-word reading achievement. Training of grapheme-phoneme associations consisted of a 20-min training program in which eight novel letters (Hebrew) needed to be paired with speech sounds taken from the participant's native language (Dutch). Eighty-four third grade students, of whom 20 were diagnosed with dyslexia, participated in the training and testing. Our results indicate a reduced ability of dyslexic readers in applying newly learned grapheme-phoneme correspondences while reading words which consist of these novel letters. However, we did not observe a significant independent contribution of grapheme-phoneme learning to reading outcomes. Alternatively, results from the regression analysis indicate that failure to read may be due to differences in phonological and/or orthographic knowledge but not to differences in the grapheme-phoneme-conversion process itself

Exploring individual user differences in the 2D/3D interaction with medical image data

User-centered design is often performed without regard to individual user differences. In this paper, we report results of an empirical study aimed to evaluate whether computer experience and demographic user characteristics would have an effect on the way people interact with the visualized medical data in a 3D virtual environment using 2D and 3D input devices. We analyzed the interaction through performance data, questionnaires and observations. The results suggest that differences in gender, age and game experience have an effect on people’s behavior and task performance, as well as on subjective\ud user preferences

Cultural Influences in Probabilistic Thinking

Concerns about students' difficulties in statistics and probability and a lack of research in this area outside of western countries led to a case study which explored form five (14 to 16 year olds) students' ideas in this area. The study focussed on probability, descriptive statistics and graphical representations. This paper presents and discusses the ways in which students made sense of probability constructs (equally likely and proportional reasoning) obtained from the individual interviews. The findings were interpreted in relation to cultural perspective. The findings revealed that many of the students used strategies based on cultural experiences (beliefs, everyday and school experiences) and intuitive strategies. While the results of the study confirm a number of findings of other researchers, the findings go beyond those discussed in the literature. The use of beliefs, everyday and school experiences was considerably more common than that discussed in literature. The paper concludes by suggesting some implications for teachers and researchers

Learning, Arts, and the Brain: The Dana Consortium Report on Arts and Cognition

Reports findings from multiple neuroscientific studies on the impact of arts training on the enhancement of other cognitive capacities, such as reading acquisition, sequence learning, geometrical reasoning, and memory

Modeling Paying Behavior in Game Social Networks

Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy

A Dynamical Systems Approach for Static Evaluation in Go

In the paper arguments are given why the concept of static evaluation has the potential to be a useful extension to Monte Carlo tree search. A new concept of modeling static evaluation through a dynamical system is introduced and strengths and weaknesses are discussed. The general suitability of this approach is demonstrated.Comment: IEEE Transactions on Computational Intelligence and AI in Games, vol 3 (2011), no