Ubiquitous learning: A systematic review

Ubiquitous learning, labeled as u–learning, takes advantage of digital content, physical surroundings, mobile devices, pervasive components, and wireless communication to deliver teaching–learning experiences to users at anytime, anywhere, and anyway. U–learning represents an emergent paradigm that spreads education in diverse settings, where users are situated in authentic learning contexts to face immersive experiences in order to accomplish meaningful learning. With the aim at disseminating such a revolutionary arena, this systematic review analyzes its nature, application, and evolution throughout a longitudinal study, where 176 approaches built since 2010 up to the third quarter of 2017 date are gathered, classified, and characterized to disclose labor traits, outcome patterns, and field tendencies. These five results are grounded respectively in a representative collection, a proposed taxonomy, a suggested pattern, statistical interpretations, mining findings, and critical analysis. The conclusions reveal: u–learning is able to transform traditional education provided at classroom level and by e–learning. Principally, this is because students, pertaining to diverse academic levels experience real and authentic settings, are immersed in dual reality sceneries, benefit from context–aware support, learn diverse educational domains, follow suitable learning paradigms, deal with diverse effects, and interact with different devices and technologies in a blended fashion. All of this with the purpose of enhancing users’ apprenticeship.     

Twitter data analysis for studying communities of practice in the media industry

Today, more and more physical communities of practice, a concept that describes a group of people that share a passion and interact regularly at events to exchange knowledge, utilize social media, such as Twitter. Brotaru, for instance, is such a physical community of practice for media professionals in Brussels. It is a monthly meet-up of videogame developers in various locations in Brussels. Furthermore, Twitter becomes widely acknowledged as important instrument for learning and community formation in the virtual world. But, do these communities of practice use Twitter only to promote their physical activities of learning? Or, are the activities of the physical communities further extended into the virtual world meaning that virtual communities of practice emerge from them? This article suggests a novel mixed-methods approach based on qualitative and quantitative data to measure the role of Twitter for physical communities of practice. The method applies different statistical measures and analysis on harvested Twitter data and additionally brings two of the most used methods in Twitter analysis together, social network analysis and text data analysis (a.k.a., content analysis). Four different communities of practice in Brussels’ media industry and their activities and followers on Twitter have been analysed. The findings showed that the activities of the communities of practice extend into the Twitter sphere as the online communities are characterised by a shared domain, a lively community and shared practices. The analysis further revealed that Twitter offers three main opportunities for the activities of communities of practice: it offers geographical extension; it gives temporal autonomy; and, it can be used to diversify the practices.     

Moderating effect of learning styles on a learning management system’s success

This study uses the DeLone and McLean model to determine the moderating impact of learning styles on the success of learning management systems from a student’s point of view. The main objectives of this research are: (1) to evaluate the Delone and McLean model of information system success in the context of learning management systems and, (2) to determine the effect of the learning styles of students on this model. An in-person survey of 258 engineering students was used to evaluate the research model. The analysis is based on structural equation modelling, specifically partial least squares. The results indicate that the research model explains use, user satisfaction, and perceived benefits of a learning management system. In addition, the Felder-Silverman learning styles (sensing-intuitive, visual-verbal; active-reflective; sequential-global) modify the strength of the relationships between the variables of the success model.     

一种自适应学习算法在图像识别中的应用研究

文章研究了特征图像的自适应识别技术问题,提出了利用自适应共振理论,实现自稳定和自组织识别的人工神经网络,达到学习和自适应识别图像的应用要求,根据这一原理及规则,给出了具有自稳定分类功能的自适应学习算法。     

Belief revision via Lamarckian evolution

We present a system for performing belief revision in a multi-agent environment. The system is called GBR (Genetic Belief Revisor) and it is based on a genetic algorithm. In this setting, different individuals are exposed to different experiences. This may happen because the world surrounding an agent changes over time or because we allow agents exploring different parts of the world. The algorithm permits the exchange of chromosomes from different agents and combines two different evolution strategies, one based on Darwin’s and the other on Lamarck’s evolutionary theory. The algorithm therefore includes also a Lamarckian operator that changes the memes of an agent in order to improve their fitness. The operator is implemented by means of a belief revision procedure that, by tracing logical derivations, identifies the memes leading to contradiction. Moreover, the algorithm comprises a special crossover mechanism for memes in which a meme can be acquired from another agent only if the other agent has “accessed” the meme, i.e. if an application of the Lamarckian operator has read or modified the meme. Experiments have been performed on the η-queen problem and on a problem of digital circuit diagnosis. In the case of the η-queen problem, the addition of the Lamarckian operator in the single agent case improves the fitness of the best solution. In both cases the experiments show that the distribution of constraints, even if it may lead to a reduction of the fitness of the best solution, does not produce a significant reduction. Evelina Lamma, Ph.D.: She is Full Professor at the University of Ferrara. She got her degree in Electrical Engineering at the University of Bologna in 1985, and her Ph.D. in Computer Science in 1990. Her research activity centers on extensions of logic programming languages and artificial intelligence. She was coorganizers of the 3rd International Workshop on Extensions of Logic Programming ELP92, held in Bologna in February 1992, and of the 6th Italian Congress on Artificial Intelligence, held in Bologna in September 1999. Currently, she teaches Artificial Intelligence and Fondations of Computer Science. Fabrizio Riguzzi, Ph.D.: He is Assistant Professor at the Department of Engineering of the University of Ferrara, Italy. He received his Laurea from the University of Bologna in 1995 and his Ph.D. from the University of Bologna in 1999. He joined the Department of Engineering of the University of Ferrara in 1999. He has been a visiting researcher at the University of Cyprus and at the New University of Lisbon. His research interests include: data mining (and in particular methods for learning from multirelational data), machine learning, belief revision, genetic algorithms and software engineering. Luís Moniz Pereira, Ph.D.: He is Full Professor of Computer Science at Departamento de Informática, Universidade Nova de Lisboa, Portugal. He received his Ph.D. in Artificial Intelligence from Brunel University in 1974. He is the director of the Artificial Intelligence Centre (CENTRIA) at Universidade Nova de Lisboa. He has been elected Fellow of the European Coordinating Committee for Artificial Intelligence in 2001. He has been a visiting Professor at the U. California at Riverside, USA, the State U. NY at Stony Brook, USA and the U. Bologna, Italy. His research interests include: knowledge representation, reasoning, learning, rational agents and logic programming.     

A review of web-based collaborative learning: factors beyond technology

Abstract Web-based collaborative learning has a large potential for knowledge acquisition. However, it has different characteristics compared with conventional learning scenarios; especially with respect to the social communication situation, message exchange, cognitive load and participation of the learners. To cope with possible problems resulting from these characteristics suitable instructional means have to be considered, like collaborative learning methods, on-line moderation, appropriate learning tasks or computer-based visualisation tools.     

基于Agent的Web学习环境模型

网上教学由于受各种条件的限制还不能达到通过传统教学获得的学习效果。因此，提出一种基于Agent的Web学习环境模型，该模型是建立在利用Agent自身特性的基础上，其目的是为了改善学习效果。     

分布式强化学习系统的体系结构研究

强化学习是一种重要的机器学习方法,随着计算机网络和分布式处理技术的飞速发展,多智能体系统中的分布式强化学习方法正受到越来越多的关注。论文将目前已有的各种分布式强化学习方法总结为中央强化学习、独立强化学习、群体强化学习、社会强化学习四类,然后探讨了这四类分布式强化学习方法的体系结构框架,并给出了这四类分布式强化学习方法的形式化定义。     

网络学习系统的开放性体系结构

基于对管理和传送学习资源过程的分析，该文提出了一个具有开放性的体系结构。通过对网络学习的过程和功能两个角度分别进行阐述，详细说明了该体系结构怎样用于解决规划网络学习以及呈现教学的最佳内容。     

一种新的Web分类方法

Web文档分类是Web挖掘中最基本的技术之一，而构造一个按照兴趣分类的分类器，需要做大量的预处理工作，来收集正负的训练样例。但负例的收集是非常困难的。文章提出了一个只有正例没有负例的学习模型。该模型主要是重复执行SVM。实验表明，该学习模型对于Web文档分类的分类精度和速度都是非常理想的。