Investigating functional thinking in the elementary classroom: Foundations of early algebraic reasoning

This paper examines the development of student functional thinking during a teaching experiment that was conducted in two classrooms with a total of 45 children whose average age was nine years and six months. The teaching comprised four lessons taught by a researcher, with a second researcher and classroom teacher acting as participant observers. These lessons were designed to enable students to build mental representations in order to explore the use of function tables by focusing on the relationship between input and output numbers with the intention of extracting the algebraic nature of the arithmetic involved. All lessons were videotaped. The results indicate that elementary students are not only capable of developing functional thinking but also of communicating their thinking both verbally and symbolically.     

基于支持向量域描述的学习分类器

文章在分析支持向量域描述的基础上发展了一类基于描述的学习分类器.该算法在训练时通过在高维特征空间中求取所描述的训练样本的超球体边界，然后通过该边界对样本数据进行分类.文章所获得的学习算法和支持向量机（SVM）和序列最小优化（SMO）算法相比，不仅降低了样本的采集代价，而且在训练速度上有了很大提高.在CBCL人脸库和USPS手写数字识别的实验中，给出了该算法和SVM、SOM算法的实验对比结果，说明了该学习算法的有效性。     

Robust estimation and classification for functional data via projection-based depth notions

Five notions of data depth are considered. They are mostly designed for functional data but they can be also adapted to the standard multivariate case. The performance of these depth notions, when used as auxiliary tools in estimation and classification, is checked through a Monte Carlo study. Research partially supported by Spanish grants MTM2004-00098 (A. Cuevas and R. Fraiman) and MTM2005-00820 (M. Febrero).     

A global perspective on an emotional learning model proposal

Existing literature argues that emotions have a significant impact on the majority of human activities and functions. The learning process is one of the activities on which emotions have a direct influence. Thus, understanding the manner in which emotions change the students’ learning process is not only very important but it can also allow to improve the existing learning models.Currently, in the majority of situations, the teacher serves as a facilitator between the student and the learning course, and through a constant analysis of the student’s behaviour, emotions and achievements, he constantly performs adjustments to the teaching process in order to meet the students’ needs and goals. Thus far, in online learning environments there is no easy way for teachers to analyse students’ behaviour and emotions. A possible solution to this problem can be the development of mechanisms that enable computers to automatically detect students’ emotions and adapt the learning process in order to meet students’ real needs.An emotional learning model was described and a software prototype was developed and tested, in order to find out whether it performs live identification of the students’ emotions, by using affective computing techniques, and whether it automatically performs adjustments to their individual learning process. Through a deeper analysis and multidisciplinary discussion of the achieved results it is possible to acknowledge that not only emotions impact students’ learning, but also that an application that performs live emotion recognition and which integrates this feature with adjustable online learning environments will trigger improvements in students’ learning.     

Reinforcement learning enhanced multi-neighborhood tabu search for the max-mean dispersion problem

This paper presents a highly effective reinforcement learning enhancement of multi-neighborhood tabu search for the max-mean dispersion problem. The reinforcement learning component uses the Q-learning mechanism that incorporates the accumulated feedback information collected from the actions performed during the search to guide the generation of diversified solutions. The tabu search component employs 1-flip and reduced 2-flip neighborhoods to collaboratively perform the neighborhood exploration for attaining high-quality local optima. A learning automata method is integrated in tabu search to adaptively determine the probability of selecting each neighborhood. Computational experiments on 80 challenging benchmark instances demonstrate that the proposed algorithm is favorably competitive with the state-of-the-art algorithms in the literature, by finding new lower bounds for 3 instances and matching the best known results for the other instances. Key elements and properties are also analyzed to disclose the source of the benefits of our integration of learning mechanisms and tabu search.     

Stock market forecasting by using a hybrid model of exponential fuzzy time series

The initial aim of this study is to propose a hybrid method based on exponential fuzzy time series and learning automata based optimization for stock market forecasting. For doing so, a two-phase approach is introduced. In the first phase, the optimal lengths of intervals are obtained by applying a conventional fuzzy time series together with learning automata swarm intelligence algorithm to tune the length of intervals properly. Subsequently, the obtained optimal lengths are applied to generate a new fuzzy time series, proposed in this study, named exponential fuzzy time series. In this final phase, due to the nature of exponential fuzzy time series, another round of optimization is required to estimate certain method parameters. Finally, this model is used for future forecasts. In order to validate the proposed hybrid method, forty-six case studies from five stock index databases are employed and the findings are compared with well-known fuzzy time series models and classic methods for time series. The proposed model has outperformed its counterparts in terms of accuracy.     

Network formation and knowledge gains

Incentive-based models for network formation link micro actions to changes in network structure. Sociologists have extended these models on a number of fronts, but there remains a tendency to treat actors as homogenous agents and to disregard social theory. Drawing upon literature on the strategic use of networks for knowledge gains, we specify models exploring the co-evolution of networks and knowledge gains. Our findings suggest that pursuing transitive ties is the most successful strategy, as more reciprocity and cycling result from this pursuit, thus encouraging learning across the network. We also discuss the role of network size, global network structure, and parameter strength in actors’ attainment of knowledge resources.     

A Bad Workman Blames His Tweets: The Consequences of Citizens' Uncivil Twitter Use When Interacting With Party Candidates

Existing studies focusing on politicians' adoption of Twitter have found that they use it primarily as a broadcasting tool. We argue that citizens' impolite and/or uncivil behavior is one possible explanation for such decisions. Social media conversations are rife with harassment and politicians are a prime target. This alters the incentive structure of engaging in dialogue on social media. We use Spanish, Greek, German, and U.K. candidates' tweets sent during the run‐up to the recent European Parliament elections, and rely on automated text analysis and machine learning methods to measure their level of civility. Our contribution is an actor‐oriented theory of political dialogue that incorporates Twitter's specific affordances, clarifying how and why Twitter's democratic promise may be limited.     

Playing with Representations: How Do Kids Make Use of Quantitative Representations in Video Games?

This paper describes the use of quantities in video games by young people as part of a broader effort to understand thinking and learning across naturally occurring contexts of activity. Our approach to investigating the use of quantities in game play is ethnographic; we have followed eight children over a six-month period as they play their own games at home. The data set is composed of video recordings and artifact-based interviews. The concept of disciplined perception is used to understand how quantities are coordinated during game play. The current study shows young people using quantities in games to make predictions and organize their actions based on those predictions. Some ideas based on the study’s findings for using video games in school are discussed.