Given the proliferation of technology sites and the growing diversity of their readership, readers are more and more likely to encounter specialized language and terminology that they may lack the sufficient background to understand. Such sites may lose readership and the experience of readers may be impacted negatively if readers cannot quickly and easily find information about terms they wish to learn more about. We developed a system using a fusion of web log mining techniques that extracts, identifies, and recommends personalized terms to readers by utilizing information found in individual and global web query logs. In addition, the system presents relevant information related to these terms inline with the text. Our system outperforms some other related systems developed in the literature with special regard to usability.

With the expansion of the internet, many specialized, high-profile sites have become available that bring very technical subject matter to readers with non-technical backgrounds. While the theme of these sites may be of interest to these readers, the posts themselves may contain terms that non-experts may be unfamiliar with and may wish to know more about. We developed PLEDS, a personalized entity detection system which identifies interesting entities and provides related information for individual users by mining web logs and query logs. The experimental results of a systemic user study shows that with PLEDS's aid, users can experience the benefits of an enriched internet surfing experience.

This paper describes an initial investigation of how different conditions for conducting a team programming exercise impact learning. We conducted a series of in-depth case studies on the use of various communication technologies and compared them with face-to-face case studies of team programming. We explored how these communication technologies can help improve students' learning. We summarize the findings from these studies and give guidance to instructors and to tool designers on how future tools can be improved to support collaborative learning in team programming.

Learning computer programming in a modern university course is rarely an individual activity; however, IDEs used in introductory programming classes do not support collaboration at a level appropriate for novices. The goal of our research is to make it easier for first-year students to experience working in a team in their programming assignments. Based on our previous work developing and evaluating IDEs for novice programmers, we have identified two main areas of required functionality: 1) features for code sharing and coordination; and 2) features to support communication. We have extended an existing teaching-oriented integrated development environment (called Gild) with features to support code sharing and coordination. We report on a preliminary study in which pairs of students used a prototype of our collaborative IDE to work on a programming assignment. The goals of this study were to evaluate the effectiveness and usability of the new features and to determine requirements for future communication support.

Sociological and technical difficulties, such as a lack of informal encounters, can make it difficult for new members of noncollocated software development teams to learn from their more experienced colleagues. To address this situation, we have developed a tool, named Hipikat that provides developers with efficient and effective access to the group memory for a software development project that is implicitly formed by all of the artifacts produced during the development. This project memory is built automatically with little or no change to existing work practices. After describing the Hipikat tool, we present two studies investigating Hipikat's usefulness in software modification tasks. One study evaluated the usefulness of Hipikat's recommendations on a sample of 20 modification tasks performed on the Eclipse Java IDE during the development of release 2.1 of the Eclipse software. We describe the study, present quantitative measures of Hipikat's performance, and describe in detail three cases that illustrate a range of issues that we have identified in the results. In the other study, we evaluated whether software developers who are new to a project can benefit from the artifacts that Hipikat recommends from the project memory. We describe the study, present qualitative observations, and suggest implications of using project memory as a learning aid for project newcomers.

The lack of lightweight communication channels and other technical and sociological difficulties make it hard for new members of a non-collocated software development team to learn effectively from their more experienced colleagues while they are coming up-to-speed on a project. To address this situation, we have developed a tool, named Hipikat, that provides developers with efficient and effective access to the group memory for a software development project that is implicitly formed by all of the artifacts produced during the development. This project memory is built automatically with little or no change to existing work practices. We report an exploratory case study evaluating whether software developers who are new to a project can benefit from the artifacts that Hipikat recommends from the project memory. To assess the appropriateness of the recommendations, we investigated when and how developers queried the project memory, how the evaluated the recommended artifacts, and the process by which they utilized the artifacts. We found that newcomers did use the recommendations and their final solutions exploited the recommended artifacts, although most of the Hipikat queries came in the early stages of a change task. We describe the case study, present qualitative observations, and suggest implications of using project memory as a learning aid for project newcomers.

A newcomer to a software project must typically come up-to-speed on a large, varied amount of information about the project before becoming productive. Assimilating this information in the open-source context is difficult because a newcomer cannot rely on the mentoring approach that is commonly used in traditional software developments. To help a newcomer to an open-source project become productive faster, we propose Hipikat, a tool that forms an implicit group memory from the information stored in a project's archives, and that recommends artifacts from the archives that are relevant to a task that a newcomer is trying to perform. To investigate this approach, we have instantiated the Hipikat tool for the Eclipse open-source project. In this paper we describe the Hipikat tool, we report on a qualitative study conducted with a Hipikat mock-up on a medium-sized in-house project, and we report on a case study in which Hipikat recommendations were evaluated for a task on Eclipse.

This paper describes an application-independent tool called Anchored Conversations that brings together text-based conversations and documents. The design of Anchored Conversations is based on our observations of the use of documents and text chats in collaborative settings. We observed that chat spaces support work conversations, but they do not allow the close integration of conversations with work documents that can be seen when people are working together face-to-face. Anchored Conversations directly addresses this problem by allowing text chats to be anchored into documents. Anchored Conversations also facilitates document sharing; accepting an invitation to an anchored conversation results in the document being automatically uploaded. In addition, Anchored Conversations provides support for review, catch-up and asynchronous communications through a database. In this paper we describe motivating fieldwork, the design of Anchored Conversations, a scenario of use, and some preliminary results from a user study.