Cognitive abilities,digital games and arithmetic performance enhancement: A study comparing the effects of a math game and paper exercises

Besides entertainment, games have shown to have the potential to impact a broader variety of cognitive abilities. Research has consistently shown that several aspects in cognition such as visual short-memory, multitasking and spatial skills can be enhanced by game play. In a previous study, it was found that playing Monkey Tales, a game aimed at training arithmetic skills, helped second grade pupils to increase their accuracy in mental calculation as compared to paper exercises. In this follow up study we explore whether traditional methods and game training differ in terms of the cognitive processes that both are able to impact. We incorporated standardized measures of working memory and visuo-motor skills. Additionally, the mathematics game was modified and its contents extracted to allow precise comparison between the gaming and paper exercises condition. Thus each single math exercise, type of question (e.g., multiple choice), quantity and order was perfectly matched in the game training and the traditional training conditions. Gains in arithmetical performance, and self-reported measures of enjoyment were also investigated. We found some evidence suggesting that arithmetic performance enhancement induced by game play and paper exercises differ not only in terms of enjoyment but also of working memory capacity improvements.     

Supporting primary students' learning of fraction conceptual knowledge through digital games

With the advent of mobile technologies, well-designed fraction apps can be used to help children gain fraction knowledge, a challenging topic for both teachers and students. The present pilot study adopted a quasi-experimental design to investigate whether children can learn fraction concepts equally well if half of the lesson time (20 min) is replaced with game-based learning. Keeping the total lesson time (40 min) identical, the control group (N = 33) received traditional instruction, and the experimental group (N = 32) was presented with a blended learning approach spending half of the class time (20 min) playing tablet-based fraction games, where each of the learners had their own tablet. The results suggested that in the posttest, the experimental group achieved similar learning gains to the control group and appear to have achieved better performance in the transfer test than the control group. This paper also discusses the efficiency of game-based learning, the mechanism of how fraction games might enhance learning, and the potential of integrating game-based learning in educational settings.     

Evolutionary collective behavior decomposition model for time series data mining

In this research, we propose a novel framework referred to as collective game behavior decomposition where complex collective behavior is assumed to be generated by aggregation of several groups of agents following different strategies and complexity emerges from collaboration and competition of individuals. The strategy of an agent is modeled by certain simple game theory models with limited information. Genetic algorithms are used to obtain the optimal collective behavior decomposition based on history data. The trained model can be used for collective behavior prediction. For modeling individual behavior, two simple games, the minority game and mixed game are investigated in experiments on the real-world stock prices and foreign-exchange rate. Experimental results are presented to show the effectiveness of the new proposed model.     

Can child-pedestrians’ hazard perception skills be enhanced?

ObjectiveTraffic collisions yield a substantial rate of morbidity and injury among child-pedestrians. We explored the formation of an innovative hazard perception training intervention – Child-pedestrians Anticipate and Act Hazard Perception Training (CA²HPT). Training was based upon enhancing participants’ ability to anticipate potential hazards by exposing them to an array of traffic scenes viewed from different angles.MethodTwenty-four 7–9-year-olds have participated. Trainees underwent a 40-min intervention of observing typical residential traffic scenarios in a simulated dome projection environment while engaging in a hazard detection task. Trainees were encouraged to note differences between the scenarios presented to them from separate angles (a pedestrian's point-of-view and a higher perspective angle). Next, trainees and control group members were required to perform crossing decision tasks.ResultsTrainees were found to be more aware of potential hazards related to restricted field of view relative to control.ConclusionsChild pedestrians are responsive to training and actively detecting materialized hazards may enrich child-pedestrians’ ability to cross roads.     

运动会新闻发布系统的设计与实现

针对运动会比赛期间随时需要方便、快捷、高效地更新大量新闻的需求 ,研制和开发一套运动会新闻发布系统。利用微软最新推出的asp .net动态网页开发工具 ,采用web分布式三层设计模型设计实现了一套操作简单、功能完善、具有通用性的运动会新闻发布系统     

Measuring College Students' Motives for Playing Drinking Games.

Students may choose to play drinking games not only for reasons related to alcohol consumption but also because of incentives related to other aspects of play (competition, fun, interpersonal dominance, etc.). College students (120 men and 167 women) completed measures of motives for playing (based on T. J. Johnson, S. Hamilton, & V. L. Sheets, 1999) and consequences of playing drinking games. Exploratory principal-components analysis identified 8 reasons for playing. Men and women differed in their endorsement of the factors. Motives for play directly predicted consequences of play independently of alcohol consumption. Specific motives predicted specific types of consequences. In multiple regression analyses, Conformity motives were negatively related to consequences and may represent a form of protective motive. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

不完全信息下的最终报价仲裁

基于争议双地仲裁人的裁决值有不同的概率估计，而这些估计是双方共同知识这一前提分析最终报价中仲裁，把它构造成一个不完全信息的非零和对策模型。然后给出了局部、全局Ｎａｓｈ平衡报价策略存在唯一的充分条件，最后探讨了双方认识存在的差异对争议人报价行为的影响。     

Neural networks for contract bridge bidding

The objective of this study is to explore the possibility of capturing the reasoning process used in bidding a hand in a bridge game by an artificial neural network. We show that a multilayer feedforward neural network can be trained to learn to make an opening bid with a new hand. The game of bridge, like many other games used in artificial intelligence, can easily be represented in a machine. But, unlike most games used in artificial intelligence, bridge uses subtle reasoning over and above the agreed conventional system, to make a bid from the pattern of a given hand. Although it is difficult for a player to spell out the precise reasoning process he uses, we find that a neural network can indeed capture it. We demonstrate the results for the case of one-level opening bids, and discuss the need for a hierarchical architecture to deal with bids at all levels.     

Editorial.

Presents some brief remarks from the editor of the Journal of Educational Psychology regarding his goals for the Journal, and noting the decline in membership in Division 15. (PsycINFO Database Record (c) 2010 APA, all rights reserved)