Social Interactions vs Revisions, What is important for Promotion in Wikipedia?

In epistemic community, people are said to be selected on their knowledge contribution to the project (articles, codes, etc.) However, the socialization process is an important factor for inclusion, sustainability as a contributor, and promotion. Finally, what does matter to be promoted? being a good contributor? being a good animator? knowing the boss? We explore this question looking at the process of election for administrator in the English Wikipedia community. We modeled the candidates according to their revisions and/or social attributes. These attributes are used to construct a predictive model of promotion success, based on the candidates's past behavior, computed thanks to a random forest algorithm. Our model combining knowledge contribution variables and social networking variables successfully explain 78% of the results which is better than the former models. It also helps to refine the criterion for election. If the number of knowledge contributions is the most important element, social interactions come close second to explain the election. But being connected with the future peers (the admins) can make the difference between success and failure, making this epistemic community a very social community too

The Impact of External Audience on Second Graders\u27 Writing Quality

The overarching purpose of writing is to communicate; as such, the intended audience is a critical consideration for writers. However, elementary school writing instruction commonly neglects the role of the audience. Typically, children are asked to compose a piece of text without a specific audience that is usually evaluated by the classroom teacher. Previous studies have found a relationship between audience specification and higher quality writing among older children; this study examines the impact of audience specification on young children’s writing. Using a within-subjects design, the study compared writing quality when second-grade students wrote for internal versus external audiences and found that children are more likely to produce higher quality writing when writing for an external audience than when writing for their teacher

Links between the personalities, styles and performance in computer programming

There are repetitive patterns in strategies of manipulating source code. For example, modifying source code before acquiring knowledge of how a code works is a depth-first style and reading and understanding before modifying source code is a breadth-first style. To the extent we know there is no study on the influence of personality on them. The objective of this study is to understand the influence of personality on programming styles. We did a correlational study with 65 programmers at the University of Stuttgart. Academic achievement, programming experience, attitude towards programming and five personality factors were measured via self-assessed survey. The programming styles were asked in the survey or mined from the software repositories. Performance in programming was composed of bug-proneness of programmers which was mined from software repositories, the grades they got in a software project course and their estimate of their own programming ability. We did statistical analysis and found that Openness to Experience has a positive association with breadth-first style and Conscientiousness has a positive association with depth-first style. We also found that in addition to having more programming experience and better academic achievement, the styles of working depth-first and saving coarse-grained revisions improve performance in programming.Comment: 27 pages, 6 figure

The afterlife of 'living deliverables': angels or zombies?

Within the STELLAR project, we provide the possibility to use living documents for the collaborative writing work on deliverables. Compared to 'normal' deliverables, 'living' deliverables come into existence much earlier than their delivery deadline and are expected to 'live on' after their official delivery to the European Commission. They are expected to foster collaboration. Within this contribution we investigate, how these deliverables have been used over the first 16 months of the project. We therefore propose a set of new analysis methods facilitating social network analysis on publicly available revision history data. With this instrumentarium, we critically look at whether the living deliverables have been successfully used for collaboration and whether their 'afterlife' beyond the contractual deadline had turned them into 'zombies' (still visible, but no or little live editing activities). The results show that the observed deliverables show signs of life, but often in connection with a topical change and in conjunction with changes in the pattern of collaboration

The CDF Data Handling System

The Collider Detector at Fermilab (CDF) records proton-antiproton collisions at center of mass energy of 2.0 TeV at the Tevatron collider. A new collider run, Run II, of the Tevatron started in April 2001. Increased luminosity will result in about 1~PB of data recorded on tapes in the next two years. Currently the CDF experiment has about 260 TB of data stored on tapes. This amount includes raw and reconstructed data and their derivatives. The data storage and retrieval are managed by the CDF Data Handling (DH) system. This system has been designed to accommodate the increased demands of the Run II environment and has proven robust and reliable in providing reliable flow of data from the detector to the end user. This paper gives an overview of the CDF Run II Data Handling system which has evolved significantly over the course of this year. An outline of the future direction of the system is given.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 7 pages, LaTeX, 4 EPS figures, PSN THKT00

Investigating the role of model-based reasoning while troubleshooting an electric circuit

We explore the overlap of two nationally-recognized learning outcomes for physics lab courses, namely, the ability to model experimental systems and the ability to troubleshoot a malfunctioning apparatus. Modeling and troubleshooting are both nonlinear, recursive processes that involve using models to inform revisions to an apparatus. To probe the overlap of modeling and troubleshooting, we collected audiovisual data from think-aloud activities in which eight pairs of students from two institutions attempted to diagnose and repair a malfunctioning electrical circuit. We characterize the cognitive tasks and model-based reasoning that students employed during this activity. In doing so, we demonstrate that troubleshooting engages students in the core scientific practice of modeling.Comment: 20 pages, 6 figures, 4 tables; Submitted to Physical Review PE

Boa: Ultra-large-scale software repository and source-code mining

In today’s software-centric world, ultra-large-scale software repositories, e.g. SourceForge, GitHub, and Google Code, are the new library of Alexandria. They contain an enormous corpus of software and related information. Scientists and engineers alike are interested in analyzing this wealth of information. However, systematic extraction and analysis of relevant data from these repositories for testing hypotheses is hard, and best left for mining software repository (MSR) experts! Specifically, mining source code yields significant insights into software development artifacts and processes. Unfortunately, mining source code at a large-scale remains a difficult task. Previous approaches had to either limit the s cope of the projects studied, limit the scope of the mining task to be more coarse-grained, or sacrifice studying the history of the code. In this paper we address mining source code: a) at a very large scale; b) at a fine-grained level of detail; and c) with full history information. To address these challenges, we present domain-specific language features for source code mining in our language and infrastructure called Boa. The goal of Boa is to ease testing MSR-related hypotheses. Our evaluation demonstrates that Boa substantially reduces programming efforts, thus lowering the barrier to entry. We also show drastic improvements in scalabilit