Some aspects of research on learning and cognitive development in Europe 1

A model of human learning processes is presented, wherein learning is conceived as a qualitative development of the structure of the learner's actions. As an illustration of this conception a concise outline of Gal'perin's theory of learning is given. In connection with the relation between cognitive development and learning some Russian research is reviewed, which provides evidence for the standpoint that cognitive development can be strongly stimulated by appropriate learning and instruction. Finally an analysis is presented of structures of action, which represent important objectives of learning to think.     

ICT和教师专业发展——瑞典案例介绍及理论框架

作为技术支持的教师专业发展（TETPD）国际比较研究的一部分,本文介绍了瑞典部分的背景、理论框架以及设计,关注的焦点是ICT与教育领域的教师专业发展（TPD）。其中,ICT被看作是一项能够支持教与学实践的技术。本文进一步的目的就是从两个维度为瑞典的多重个案研究提供一个框架：第一个维度是关于教师专业发展的权属和管理问题,既包括由上而下的政府项目,也包括由下而上由参与者推动的计划;第二个维度是关于教师专业发展的方法或者模式问题,既包含传统的面对面模式,也包含在线学习模式。通过该框架,我们选出了四个使用ICT进行教师专业发展的项目作为案例,并将其与关于TPD模型和项目的不同观点,以及Villegas-Reimers对TPD的新观点联系起来。最后,本文还讨论了这些项目在支持教与学中的ICT应用方面的意义和前景。     

Bayesian network based extreme learning machine for subjectivity detection

Subjectivity detection is a task of natural language processing that aims to remove ‘factual’ or ‘neutral’ content, i.e., objective text that does not contain any opinion, from online product reviews. Such a pre-processing step is crucial to increase the accuracy of sentiment analysis systems, as these are usually optimized for the binary classification task of distinguishing between positive and negative content. In this paper, we extend the extreme learning machine (ELM) paradigm to a novel framework that exploits the features of both Bayesian networks and fuzzy recurrent neural networks to perform subjectivity detection. In particular, Bayesian networks are used to build a network of connections among the hidden neurons of the conventional ELM configuration in order to capture dependencies in high-dimensional data. Next, a fuzzy recurrent neural network inherits the overall structure generated by the Bayesian networks to model temporal features in the predictor. Experimental results confirmed the ability of the proposed framework to deal with standard subjectivity detection problems and also proved its capacity to address portability across languages in translation tasks.     

Commercially available compression garments or electrical stimulation do not enhance recovery following a sprint competition in elite cross-country skiers

This study investigated whether commercially available compression garments (COMP) exerting a moderate level of pressure and/or neuromuscular electrical stimulation (NMES) accelerate recovery following a cross-country sprint skiing competition compared with a control group (CON) consisting of active recovery only. Twenty-one senior (12 males, 9 females) and 11 junior (6 males, 5 females) Swedish national team skiers performed an outdoor sprint skiing competition involving four sprints lasting ～3–4 min. Before the competition, skiers were matched by sex and skiing level (senior versus junior) and randomly assigned to COMP (n?=?11), NMES (n?=?11) or CON (n?=?10). Creatine kinase (CK), urea, countermovement jump (CMJ) height, and perceived muscle pain were measured before and 8, 20, 44 and 68?h after competition. Neither COMP nor NMES promoted the recovery of blood biomarkers, CMJ or perceived pain post-competition compared with CON (all P?>?.05). When grouping all 32 participants, urea and perceived muscle pain increased from baseline, peaking at 8?h (standardised mean difference (SMD), [95% confidence intervals (CIs)]): 2.8 [2.3, 3.2]) and 44?h (odds ratio [95% CI]: 3.3 [2.1, 5.1]) post-competition, respectively. Additionally, CMJ was lower than baseline 44 and 68?h post-competition in both males and females (P?相似文献   

A machine learning approach to sentiment analysis in multilingual Web texts

Sentiment analysis, also called opinion mining, is a form of information extraction from text of growing research and commercial interest. In this paper we present our machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and written in English, Dutch and French. We train from a set of example sentences or statements that are manually annotated as positive, negative or neutral with regard to a certain entity. We are interested in the feelings that people express with regard to certain consumption products. We learn and evaluate several classification models that can be configured in a cascaded pipeline. We have to deal with several problems, being the noisy character of the input texts, the attribution of the sentiment to a particular entity and the small size of the training set. We succeed to identify positive, negative and neutral feelings to the entity under consideration with ca. 83% accuracy for English texts based on unigram features augmented with linguistic features. The accuracy results of processing the Dutch and French texts are ca. 70 and 68% respectively due to the larger variety of the linguistic expressions that more often diverge from standard language, thus demanding more training patterns. In addition, our experiments give us insights into the portability of the learned models across domains and languages. A substantial part of the article investigates the role of active learning techniques for reducing the number of examples to be manually annotated.     

Future programs at the National Library of Medicine

The future of the National Library of Medicine will be shaped by a number of scientific, technical, and social influences. Among these are the continuing rapid development of computer technology and storage systems. Artificial intelligence techniques, factual databases, the emergence of medical informatics as a formal discipline, and the development of Integrated Academic Information Management Systems (IAIMS) are also important influences on the direction of the library. Public policy issues will influence the future of NLM--among them, sharing of scientific information between nations and the role of federal agencies in dissemination of information domestically. The formal, long-range plan now being prepared for the library by panels of expert advisers will be a guide for future programs and goals.     

Everyday personal laptop usage in secondary schools in Trinidad and Tobago

The e-Connect And Learn (eCAL) programme is a Trinidad and Tobago government-led initiative, launched in 2010, that grants personal laptop computers to students entering secondary school. The purpose of this paper is to provide a snapshot of how students are using these government-issued personal laptops 3 years after the programme’s launch. This paper explores the fissure between what the policy recommends and what is actually enacted in the context of laptop usage in the classroom. Data were drawn from 1,451 students, from 32 secondary schools across Trinidad and Tobago. Responses indicate that students are falling short of the expectations for in-class laptop usage and in schools where usage is less often but on a regular basis, laptops are mainly used for technology-related subjects. These findings not only suggest that students’ everyday personal laptop usage in the classroom is not in alignment with the eCAL programme goals, but also highlight some of the issues to be considered by other countries when implementing large-scale laptop programmes.     

Research-based knowledge: researchers’ contribution to evidence-based practice and policy making in career guidance

To present evidence for the outcomes of career guidance is increasingly seen as pivotal for a further professionalization of policy making and service provision. This paper puts an emphasis on researchers’ contribution to evidence-based practice and policy making in career guidance. We argue for a broader and more pluralistic research strategy to meet the complexity of possible questions needed to be asked to provide evidence in career guidance. The article puts a specific focus on the need for a stronger involvement of the voice of users.     

Teacher autonomy within a flexible national curriculum: development of Shoah (holocaust) education in Israeli state schools

This article considers the role of teacher agency and curricular flexibility as pedagogic features of Shoah education in Israeli state schools. The analysis is based on a recent national study which included a quantitative survey (questionnaires), qualitative methods (focus groups, interviews, observations) and a socio-historical review. As teaching of this subject has expanded in both religious and general streams of the Hebrew-language state school system, it has been addressed in diverse ways in terms of method, materials and content. Loosely defined requirements enable educators to be active agents in individualizing the curriculum. It is proposed that this is beneficial when addressing an inherently sensitive subject that is tied to teachers’ and students’ sense of identity and worldview. Implications of a new national curriculum for Shoah education are discussed. Relevance of teacher agency in Shoah education in other settings is considered.