Coordination processes in computer supported collaborative writing

In the COSAR-project a computer-supported collaborative learning environment enables students to collaborate in writing an argumentative essay. The TC3 groupware environment (TC3: Text Composer, Computer supported and Collaborative) offers access to relevant information sources, a private notepad, a chat facility including a chat history, and a shared word-processor. Planning tools for writing – a shared argumentation diagram for content generation and a shared outline facility for content linearization – were added to the basic TC3 environment. About 145 pairs of high school students completed essays on organ donation or cloning in the TC3 environment. We analyzed the logged discussion (‘chats’) and activity protocols for task-related processes present during discussion and collaboration. Processes looked into are planning, gathering information and composing the essay, as well as collaborative processes such as coordinating, turn taking and time management. Our main research question is how task-related planning activities and collaborative coordination with or without the help of planning tools relate to the quality of the resulting argumentative texts. Overall coordination and planning of the writing activities on a meta-level and on a content level were found to be crucial for the quality of the text.     

Investigating teamwork and taskwork in single- and multi-display groupware systems

Multi-display groupware (MDG) systems, which typically comprise both public and personal displays, promise to enhance collaboration, yet little is understood about how they differ in use from single-display groupware (SDG) systems. While research has established the technical feasibility of MDG systems, evaluations have not addressed the question of how users’ behave in such environments, how their interface design can impact group behavior, or what advantages they offer for collaboration. This paper presents a user study that investigates the impact of display configuration and software interface design on taskwork and teamwork. Groups of three completed a collaborative optimization task in single- and multi-display environments, under different task interface constraints. Our results suggest that MDG configurations offer advantages for performing individual task duties, whereas SDG conditions offer advantages for coordinating access to shared resources. The results also reveal the importance of ergonomic design considerations when designing co-located groupware systems.     

Construction of interaction observation systems for collaboration analysis in groupware applications

Interaction observation systems for groupware applications capture and process all the actions performed by users engaged in workgroups. These actions are then stored in log documents that enable the work process carried out by the users to be analyzed and the interaction between users to be studied. This article proposes an approach, based on ontological models, which is devised to help the developer of an observation system for a groupware application to structure and record user actions. In order to achieve this aim, we present a specific ontology that shapes the collaborative work process of the users so as to obtain an XML-based log document that stores all the actions carried out by the users and facilitates the subsequent analysis of the system usage and users’ behavior. This approach has been used to improve communication and collaboration capabilities in the COLLECE groupware application.     

Mutual engagement and collocation with shared representations

In this paper we explore the use of shared representations to support creative activities, focussing on collaborative music making. We examine the effect that user interface features of shared representations have on mutual engagement and show that providing shared awareness mechanisms increases mutual engagement. In particular, we show through an empirical study of 78 participants that providing cues to identity and shared pointers increases mutual engagement between participants, but together these features can overwhelm users. We also demonstrate that support for free-form annotation and spatial interaction with shared representations mediates interaction and helps participants to manage their collaborative activity effectively. In this paper we develop several measures mutual engagement and demonstrate their use to assess the design of shared representations. A key contribution of this paper is the development of a measure of collocation of participant interaction which indicates mutual engagement. The findings of the study have implications beyond the domain of collaborative music making and we outline some design guidelines for mutually engaging shared representations.     

Understanding children's collaborative interactions in shared environments

  Traditional computer technology offers limited support for face-to-face, synchronous collaboration. Consequently, children who wish to collaborate while using computers must adapt their interactions to the single-user paradigm of most personal computers. Recent technological advances have enabled the development of co-located groupware systems offering support for concurrent, multi–user interactions around a shared display. These systems provide a unique collaboration environment in which users share both the physical and the virtual workspace. This paper examines how such technology impacts children's collaboration. Findings from this research show that when concurrent, multi–user interaction is supported on a shared display, children exhibit collaborative behaviour similar to their interactions during paper-based activities. The findings also suggest strengths and weaknesses of various mechanisms for supporting synchronous interactions that have implications for the design of computer systems to support children's face-to-face collaboration.     

基于知识图谱和图注意力的众包任务推荐算法

为解决目前众包任务推荐存在未考虑任务文本信息和数据稀疏的问题,提出一种基于知识图谱与图注意力的众包任务推荐模型。该模型首先利用自然语言处理技术提取任务文本信息中的关键要素,用于丰富图谱信息和缓解数据稀疏性;通过融合用户—任务交互图中的协同信息来构建协同知识图谱,在协同知识图谱中按协同邻居的类型分别运用图注意力网络;为获取用户准确的偏好,聚合邻居信息时按注意力得分从高到低采样固定数目的邻居;最后通过聚合不同类型的协同信息生成用户和任务的嵌入表示并得到交互概率。在构建的众包数据集上进行实验的结果表明,该模型在AUC、精准率、召回率和NDCG四个指标上均优于基线模型,验证了模型的可行性和有效性。     

Creating user-adapted Websites by the use of collaborative filtering

The information globalization induced by the rapid development of the Internet and the accompanying adoption of the Web throughout the society leads to Websites which reach large audiences. The diversity of the audiences and the need of customer retention require active Websites, which expose themselves in a customized or personalized way: We call those sites User-adapted Websites. New technologies are necessary to personalize and customize content. Information filtering can be used for the discovery of important content and is therefore a key-technology for the creation of user-adapted Websites.

In this article, we focus on the application of collaborative filtering for user-adapted Websites. We studied techniques for combining and integrating content-based filtering with collaborative filtering in order to address typical problems in collaborative filtering systems and to improve the performance. Other issues which are mentioned but only lightly covered include user interface challenges. To validate our approaches we developed a prototype user-adapted Website, the Active WebMuseum, a museum Website, which exposes its collection in a personalized way by the use of collaborative filtering.     

Collaborative user modeling for enhanced content filtering in recommender systems

Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of content suited to their needs. To provide proper recommendations to users, personalized recommender systems require accurate user models of characteristics, preferences and needs. In this study, we propose a collaborative approach to user modeling for enhancing personalized recommendations to users. Our approach first discovers useful and meaningful user patterns, and then enriches the personal model with collaboration from other similar users. In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user model based on collaborative filtering approaches, and a vector space model. We present experimental results that show how our model performs better than existing alternatives.