Supporting community-based learning: case study of a geographical community organization designing its website

Community computing supports human - computer interaction among neighbours in geographical or place-based community organizations. Using a case study of such an organization, we investigate the process of designing their website. Our long-term participatory design approach, integrating developmental informal learning, allowed us to understand how this community organization adopts, evaluates, and sustains website technology. Based on our case study analysis, we present three design heuristics for developing community-based technology: align and afford new possibilities for participation, dynamically manage organizational knowledge and learning, and enhance social capital within community organizations and with the broader community.     

Information enhancement for interpreting competitive learning

In this paper, we propose a new method called information enhancement to interpret internal representations of competitive learning. We consider competitive learning as a process of mutual information maximisation on input patterns. Then, we examine to what extent this mutual information can be increased or decreased by focusing upon or enhancing some elements in a network. If this enhancement for the elements increases information on input patterns, these elements possess more information on input patterns. Thus, we only have to carefully examine those elements in a network. We applied the method to an artificial problem, the Iris problem and an air pollution problem. In all problems, we succeeded in extracting important features in patterns. In addition, final maps were better than those obtained by the conventional self-organising map. We can say that this is the first step towards the full understanding of internal representations in competitive learning.     

Enhancing and Relaxing Competitive Units for Feature Discovery

In this paper, we propose a new information-theoretic method called enhancement and relaxation to discover main features in input patterns. We have so far shown that competitive learning is a process of mutual information maximization between input patterns and connection weights. However, because mutual information is an average over all input patterns and competitive units, it is not adequate for discovering detailed information on the roles of elements in a network. To extract information on the roles of elements in a networks, we enhance or relax competitive units through the elements. Mutual information should be changed by these processes. The change in information is called enhanced information. The enhanced information can be used to discover features in input patterns, because the information includes detailed information on elements in a network. We applied the method to the symmetry data, the well-known Iris problem and the OECD countries classification. In all cases, we succeeded in extracting the main features in input patterns.     

Improving information-theoretic competitive learning by accentuated information maximization

In this paper, we propose a new computational method for information-theoretic competitive learning. We have so far developed information-theoretic methods for competitive learning in which competitive processes can be simulated by maximizing mutual information between input patterns and competitive units. Though the methods have shown good performance, networks have had difficulty in increasing information content, and learning is very slow to attain reasonably high information. To overcome the shortcoming, we introduce the rth power of competitive unit activations used to accentuate actual competitive unit activations. Because of this accentuation, we call the new computational method “accentuated information maximization”. In this method, intermediate values are pushed toward extreme activation values, and we have a high possibility to maximize information content. We applied our method to a vowel–consonant classification problem in which connection weights obtained by our methods were similar to those obtained by standard competitive learning. The second experiment was to discover some features in a dipole problem. In this problem, we showed that as the parameter r increased, less clear representations could be obtained. For the third experiment of economic data analysis, much clearer representations were obtained by our method, compared with those obtained by the standard competitive learning method.     

Component-based end-user database design for ecologists

To solve today’s ecological problems, scientists need well documented, validated, and coherent data archives. Historically, however, ecologists have collected and stored data idiosyncratically, making data integration even among close collaborators difficult. Further, effective ecology data warehouses and subsequent data mining require that individual databases be accurately described with metadata against which the data themselves have been validated. Using database technology would make documenting data sets for archiving, integration, and data mining easier, but few ecologists have expertise to use database technology and they cannot afford to hire programmers. In this paper, we identify the benefits that would accrue from ecologists’ use of modern information technology and the obstacles that prevent that use. We describe our prototype, the Canopy DataBank, through which we aim to enable individual ecologists in the forest canopy research community to be their own database programmers. The key feature that makes this possible is domain-specific database components, which we call templates. We also show how additional tools that reuse these components, such as for visualization, could provide gains in productivity and motivate the use of new technology. Finally, we suggest ways in which communities might share database components and how components might be used to foster easier data integration to solve new ecological problems.     

Strategy of information management (?)

Information management seems to be the “in” subject of the decade. However, it remains an elusive target. It is suggested that the problem lies in the assumption that we are dealing with information management, as of it were solely concerned with the supply of information. We argue that it would be more fruitful to think in terms of managing information technology, in view of its perceived increasing strategic interest to organizations. The subject is treated from both an historical and organizational perspective.For the time being, the ideal and reality still seem to be poles apart. On the one hand, we find that, despite a serious lack of sound insight, information technology has already been absorbed by most organizations; on the other hand, it is clear that for today's strategy, simple extrapolation does not suffice. We are learning how to bridge the gap in understanding, so that information technology may be actively exploited to strengthen whatever other activities an organization seems to find important for its competitive advantage.     

Community computing and citizen productivity

This article is about the development of a community network in an Atlanta, Georgia, neighborhood. The project was a long-term effort using community-based design with the goal of helping a community use information technologies to enhance more effectively the life of the community. The argument is about designing information technology tools in community contexts, developing new models of research for community-based work, and about the critical importance of engaging in community-based work that can be sustained over time. The argument also focuses on the necessity of designing community networks that both recognize the productive power and expertise of community residents as well as allow for productive practices to be developed and used in the future.     

Starting open source collaborative innovation: the antecedents of network formation in community source

Specific needs in the area of enterprise applications have led to a new type of collaborative open source innovation development across institution borders: community source. We use the Kuali community source network, a jointly managed, border‐spanning organization that supplies the institutions that created it, to describe how community source works. This study builds a theoretical basis for understanding the individual and institutional factors affecting community source network formation and the decision by organizations to join a community source network. We identify eight antecedents of decisions about forming or joining community source initiatives: motives, learning, trust, norms and monitoring, institutional similarity, external funding, hostile external environment and information technology. © 2016 John Wiley & Sons Ltd     

The role of a location-based city exploration game in digital placemaking

ABSTRACT

Many digital technologies, such as social media, community systems, and public displays, have been studied to explore how people engage with each other in their community. Yet little is known about how one form of technology, location-based games (LBGs), can support urban residents in community awareness, city exploration, and placemaking as they navigate spaces and places in their cities. To explore this topic, we investigated the challenges urban residents faced in finding information about their community along a transit network. We then designed, developed, and evaluated an LBG called City Explorer that supports city exploration using gamification and the viewing and sharing of community information. We found that residents valued the fun, competition, and rewards afforded through play in public spaces, creating opportunities for placemaking through location services and knowledge sharing. Players also wanted additional knowledge about their transit commutes, including data about the frequency and routines of their transit rides. Collectively, such ridership data offers potential for smart city initiatives and illustrates that careful design considerations are required to balance people’s needs for play, personal data, privacy, and community information acquisition.     

Conversational learning integration in technology enhanced classrooms

Today’s college students have grown up with technology. These digital natives typically gravitate toward group activities in technology embedded social contexts. However, despite this multidimensional evolution, little has changed in conventional classrooms where they build their education experience. We investigate learning models in a classroom environment which still remains the main driver of education today. We describe a conversational learning model based on group activities which involve multi-party conversations. We implement this model in a technology-enhanced studio-classroom to “visualize” conversations which otherwise would remain abstract to learners. Teachers are empowered with instructional patterns to guide their changing role in this novel classroom environment. Based on standard assessment indicators, we conduct an experimental analysis which results show interesting tradeoffs of learning performance that favor the proposed conversational learning approach compared to those obtained from conventional instruction.     

Identifying composite crosscutting concerns through semi‐supervised learning

Aspect mining improves the modularity of legacy software systems through identifying their underlying crosscutting concerns (CCs). However, a realistic CC is a composite one that consists of CC seeds and relative program elements, which makes it a great challenge to identify a composite CC. In this paper, inspired by the state‐of‐the‐art information retrieval techniques, we model this problem as a semi‐supervised learning problem. First, the link analysis technique is adopted to generate CC seeds. Second, we construct a coupling graph, which indicates the relationship between CC seeds. Then, we adopt community detection technique to generate groups of CC seeds as constraints for semi‐supervised learning, which can guide the clustering process. Furthermore, we propose a semi‐supervised graph clustering approach named constrained authority‐shift clustering to identify composite CCs. Two measurements, namely, similarity and connectivity, are defined and seeded graph is generated for clustering program elements. We evaluate constrained authority‐shift clustering on numerous software systems including large‐scale distributed software system. The experimental results demonstrate that our semi‐supervised learning is more effective in detecting composite CCs. Copyright © 2013 John Wiley & Sons, Ltd.     

Hidden in plain sight: low-literacy adults in a developed country overcoming social and educational challenges through mobile learning support tools

Illiteracy is often associated with people in developing countries. However, an estimated 50 % of adults in a developed country such as Canada lack the literacy skills required to cope with the challenges of today’s society; for them, tasks such as reading, understanding, basic arithmetic, and using everyday items are a challenge. Many community-based organizations offer resources and support for these adults, yet overall functional literacy rates are not improving. This is due to a wide range of factors, such as poor retention of adult learners in literacy programs, obstacles in transferring the acquired skills from the classroom to the real life, personal attitudes toward learning, and the stigma of functional illiteracy. In our research we examined the opportunities afforded by personal mobile devices in providing learning and functional support to low-literacy adults. We present the findings of an exploratory study aimed at investigating the reception and adoption of a technological solution for adult learners. ALEX^© is a mobile application designed for use both in the classroom and in daily life in order to help low-literacy adults become increasingly literate and independent. Such a solution complements literacy programs by increasing users’ motivation and interest in learning, and raising their confidence levels both in their education pursuits and in facing the challenges of their daily lives. We also reflect on the challenges we faced in designing and conducting our research with two user groups (adults enrolled in literacy classes and in an essential skills program) and contrast the educational impact and attitudes toward such technology between these. Our conclusions present the lessons learned from our evaluations and the impact of the studies’ specific challenges on the outcome and uptake of such mobile assistive technologies in providing practical support to low-literacy adults in conjunction with literacy and essential skills training.     

In-depth behavior understanding and use: The behavior informatics approach

The in-depth analysis of human behavior has been increasingly recognized as a crucial means for disclosing interior driving forces, causes and impact on businesses in handling many challenging issues such as behavior modeling and analysis in virtual organizations, web community analysis, counter-terrorism and stopping crime. The modeling and analysis of behaviors in virtual organizations is an open area. Traditional behavior modeling mainly relies on qualitative methods from behavioral science and social science perspectives. On the other hand, so-called behavior analysis is actually based on human demographic and business usage data, such as churn prediction in the telecommunication industry, in which behavior-oriented elements are hidden in routinely collected transactional data. As a result, it is ineffective or even impossible to deeply scrutinize native behavior intention, lifecycle and impact on complex problems and business issues. In this paper, we propose the approach of behavior informatics (BI), in order to support explicit and quantitative behavior involvement through a conversion from source data to behavioral data, and further conduct genuine analysis of behavior patterns and impacts. BI consists of key components including behavior representation, behavioral data construction, behavior impact analysis, behavior pattern analysis, behavior simulation, and behavior presentation and behavior use. We discuss the concepts of behavior and an abstract behavioral model, as well as the research tasks, process and theoretical underpinnings of BI. Two real-world case studies are demonstrated to illustrate the use of BI in dealing with complex enterprise problems, namely analyzing exceptional market microstructure behavior for market surveillance and mining for high impact behavior patterns in social security data for governmental debt prevention. Substantial experiments have shown that BI has the potential to greatly complement the existing empirical and specific means by finding deeper and more informative patterns leading to greater in-depth behavior understanding. BI creates new directions and means to enhance the quantitative, formal and systematic modeling and analysis of behaviors in both physical and virtual organizations.     

Community-organizing agent: An artificial intelligent system for building learning communities among large numbers of learners

Web-based (or online) learning provides an unprecedented flexibility and convenience to both learners and instructors. However, large online classes relying on instructor-centered presentations could tend to isolate many learners. The size of these classes and the wide dispersion of the learners make it challenging for instructors to interact with individual learners or to facilitate learner collaborations. Since extensive literature has confirmed that the substantial impact of learner interaction on learning outcomes, it is pedagogically critical to help distributed learners engage in community-based collaborative learning and to help individual learners improve their self-regulation. The E-learning lab of Shanghai Jiaotong University created an artificial intelligence system to help guide learners with similar interests into reasonably sized learning communities. The system uses a multi-agent mechanism to organize and reorganize supportive communities based on learners’ learning interests, experiences, and behaviors. Through effective award and exchange algorithms, learners with similar interests and experiences will form a community to support each others’ learning. Simulated experimental results indicate that these algorithms can improve the speed and efficiency in identifying and grouping homogeneous learners. Here, we will describe this system in detail and present its mechanism for organizing learning communities. We will conduct human experimentations in the near future to further perfect the system.     

Measuring perceived sociability of computer-supported collaborative learning environments

Most asynchronous computer-supported collaborative learning (CSCL) environments can be characterized as functional environments because they focus on functional, task-specific support, often disregarding explicit support for the social (emotional) aspects of learning in groups which are acknowledged by many educational researchers to be essential for effective collaborative learning. In contrast, sociable CSCL environments emphasize the social (emotional) aspects of group learning. We define sociability as the extent to which a CSCL environment is perceived to be able to facilitate the emergence of a sound social space with attributes as trust and belonging, a strong sense of community, and good working relationships. Specific environmental characteristics, which we have designated social affordances, determine sociability.     

Corporate management's role in system development

Assimilation of computer technology by organizations is an organizational learning process that has unique characteristics. Organizations are in different stages of development. These stages provide the foundation for developing adequate situation management emphasis.This paper is based on experience gained in the author auditing and consultancy practice. It discusses some underlying factors that nowadays in typical organizations seem to drive the assimilation process. The consequences that arise with regard to appropriate management emphasis are highlighted.     

基于虚拟学习社区的智能学习算法研究

随着信息技术和互联网技术的发展,虚拟学习社区为学习者提供一个开放的学习环境,是满足时代教育要求的强有力的平台,成为学习者网络交流、网络学习的重要手段。如何利用虚拟学习社区来提高学习者的学习效率成为当前研究的热点和重点。本文基于虚拟学习社区,提出一种智能的个性化推荐学习算法,使学习者能够方便、有效、智能、反思性学习,为构建虚拟学习社区提供理论技术支撑,以便更好地指导虚拟学习社区的学习实践。     

支持创新型组织学习:基于任务情景的知识适用性管理

面临商务环境的快速变迁和难以预测,创新型组织学习正在成为组织提高生存、适应和竞争能力的关键途径.然而,现有的KM技术对于如何支持创新型组织学习,没有进行深入、系统的研究;导致知识工作者在获取适用性好的信息体(从而促进业务创新)和主动参与OM开放性进化(以促进创新知识的传播和共享)方面缺乏有效的支持.文中提出基于任务情景的知识适用性管理方法KAMTC,旨在以基于应用域本体的任务情景描述模式作为统一的语义基础,去确切、全面和便捷地描述业务工作的查询需求和受检信息体的适用性,以克服这些挑战性问题,进而有效支持知识创新和创新型组织学习的展开.     

Collaborative information environments to support knowledge construction by communities

Computer-based design environments for skilled domain workers have recently graduated from research prototypes to commercial products, supporting the learning of individual designers. Such systems do not, however, adequately support the collaborative nature of work or the evolution of knowledge within communities of practice. If innovation is to be supported within collaborative efforts, thesedomain-oriented design environments (DODEs) must be extended to becomecollaborative information environments (CIEs), capable of providing effective community memories for managing information and learning within constantly evolving collaborative contexts. In particular, CIEs must provide functionality that facilitates the construction of new knowledge and the shared understanding necessary to use this knowledge effectively within communities of practice.This paper reviews three stages of work on artificial (computer-based and Web-based) systems that augment the intelligence of people and organisations. NetSuite illustrates the DODE approach to supporting the work of individual designers with learning-on-demand. WebNet extends this model to CIEs that support collaborative learning by groups of designers. Finally, WebGuide shows how a computational perspectives mechanism for CIEs can support the construction of knowledge and of shared understanding within groups. According to recent theories of cognition, human intelligence is the product of tool use and of social mediations as well as of biological development; CIEs are designed to enhance this intelligence by providing computationally powerful tools that are supportive of social relations.