A collaborative object represents a data type (such as a text document or a spreadsheet) designed to be shared by multiple geographically separated users. In order to improve performance and availability of data in such a distributed context, each user has a local copy of the shared objects, upon which he may perform updates. Locally executed updates are then transmitted to the other users. So,...

This paper presents the methodology, design principles and detailed evaluation of a new freely available multilayer corpus, collected and edited via classroom annotation using collaborative software. After briefly discussing corpus design for open, extensible corpora, five classroom annotation projects are presented, covering structural markup in TEI XML, multiple part of speech tagging, consti...

Cleogo is a novel groupware environment that allows several users to simultaneously develop programs through any mixture of three alternative programming metaphors: a direct manipulation language for programming by demonstration; an iconic language; and a standard text-based language. Cleogo is motivated by the pedagogical values of peer-learning and of collaborative problem solving, and by our...

This paper contributes to the design of Groupware Calendar Systems (GCSs) for use in domestic life. We consider a number of ethnographic studies of calendar use in domestic circumstances to illuminate the design space and inform design reasoning. GCSs have been employed in the workplace for sometime and have been informed by studies of ‘calendar work’. As design moves out of the workplace and i...

We develop a technique we call spectral filtering, for discovering high-quality topical resources in hyperlinked corpora. Through relevance and quality judgements collected from 37 users, we show that, over 26 topics, spectral filtering usually finds web pages that are rated better than those returned by the hand-compiled Yahoo! resource list, and by the Altavista search engine.

Collaborative filtering (CF) and contentbased filtering (CBF) have widely been used in information filtering applications, both approaches having their individual strengths and weaknesses. This paper proposes a novel probabilistic framework to unify CF and CBF, named collaborative ensemble learning. Based on content based probabilistic models for each user’s preferences (the CBF idea), it combi...

Collaborative software development presents a variety of coordination and communication problems, particularly when teams are geographically distributed. One reason for these problems is the difficulty of staying aware of others – keeping track of information about who is working on the project, who is active, and what tasks people have been working on. Current software development environments...

Numerous initiatives have allowed users to share knowledge or opinions using collaborative platforms. In most cases, the users provide a textual description of their knowledge, following very limited or no constraints. Here, we tackle the classification of documents written in such an environment. As a use case, our study is made in the context of text mining evaluation campaign material, relat...

Human beings, not machines, are the ultimate experts for information retrieval tasks, including recommender systems. Consequently, computers are most useful when they combine information about people’s judgments. Collaborative filtering systems make use of this observation by having users explicitly rate items, such as Web pages, with the system making recommendations to other users based on ov...

E-learning is popularized so fast and Collaborative Learning (CL) becomes so important an instructional strategy. There are huge Group Session (GS) texts needed to be analyzed to evaluate CL, thus the automatic or semi-automatic methods of analyzing the GS texts become very important. In this paper we present a method called Interaction Analysis depended on Knowledge Extraction (IAKE) to analyz...