skip to main content


Title: The Wikipedia Adventure: Field Evaluation of an Interactive Tutorial for New Users
Integrating new users into a community with complex norms presents a challenge for peer production projects like Wikipedia. We present The Wikipedia Adventure (TWA): an interactive tutorial that offers a structured and gamified introduction to Wikipedia. In addition to describing the design of the system, we present two empirical evaluations. First, we report on a survey of users, who responded very positively to the tutorial. Second, we report results from a large-scale invitation-based field experiment that tests whether using TWA increased newcomers' subsequent contributions to Wikipedia. We find no effect of either using the tutorial or of being invited to do so over a period of 180 days. We conclude that TWA produces a positive socialization experience for those who choose to use it, but that it does not alter patterns of newcomer activity. We reflect on the implications of these mixed results for the evaluation of similar social computing systems.  more » « less
Award ID(s):
1617129
NSF-PAR ID:
10039617
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the 20th ACM Conference on Computer-Supported Cooperative Work & Social Computing (CSCW ’17)
Page Range / eLocation ID:
1785 to 1799
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. A growing body of work has highlighted the important role that Wikipedia's volunteer-created content plays in helping search engines achieve their core goal of addressing the information needs of hundreds of millions of people. In this paper, we report the results of an investigation into the incidence of Wikipedia links in search engine results pages (SERPs). Our results extend prior work by considering three U.S. search engines, simulating both mobile and desktop devices, and using a spatial analysis approach designed to study modern SERPs that are no longer just "ten blue links". We find that Wikipedia links are extremely common in important search contexts, appearing in 67-84% of desktop SERPs for common and trending queries, but less often for medical queries. Furthermore, we observe that Wikipedia links often appear in "Knowledge Panel" SERP elements and are in positions visible to users without scrolling, although Wikipedia appears less often and in less prominent positions on mobile devices. Our findings reinforce the complementary notions that (1) Wikipedia content and research has major impact outside of the Wikipedia domain and (2) powerful technologies like search engines are highly reliant on free content created by volunteers. 
    more » « less
  2. User-generated content sites routinely block contributions from users of privacy-enhancing proxies like Tor because of a perception that proxies are a source of vandalism, spam, and abuse. Although these blocks might be effective, collateral damage in the form of unrealized valuable contributions from anonymity seekers is invisible. One of the largest and most important user-generated content sites, Wikipedia, has attempted to block contributions from Tor users since as early as 2005. We demonstrate that these blocks have been imperfect and that thousands of attempts to edit on Wikipedia through Tor have been successful. We draw upon several data sources and analytical techniques to measure and describe the history of Tor editing on Wikipedia over time and to compare contributions from Tor users to those from other groups of Wikipedia users. Our analysis suggests that although Tor users who slip through Wikipedia's ban contribute content that is more likely to be reverted and to revert others, their contributions are otherwise similar in quality to those from other unregistered participants and to the initial contributions of registered users. 
    more » « less
  3. User-generated content sites routinely block contributions from users of privacy-enhancing proxies like Tor because of a perception that proxies are a source of vandalism, spam, and abuse. Although these blocks might be effective, collateral damage in the form of unrealized valuable contributions from anonymity seekers is invisible. One of the largest and most important user-generated content sites, Wikipedia, has attempted to block contributions from Tor users since as early as 2005. We demonstrate that these blocks have been imperfect and that thousands of attempts to edit on Wikipedia through Tor have been successful. We draw upon several data sources and analytical techniques to measure and describe the history of Tor editing on Wikipedia over time and to compare contributions from Tor users to those from other groups of Wikipedia users. Our analysis suggests that although Tor users who slip through Wikipedia's ban contribute content that is more likely to be reverted and to revert others, their contributions are otherwise similar in quality to those from other unregistered participants and to the initial contributions of registered users. 
    more » « less
  4. In recent years, the pace of innovations in the fields of machine learning (ML) has accelerated, researchers in SysML have created algorithms and systems that parallelize ML training over multiple devices or computational nodes. As ML models become more structurally complex, many systems have struggled to provide all-round performance on a variety of models. Particularly, ML scale-up is usually underestimated in terms of the amount of knowledge and time required to map from an appropriate distribution strategy to the model. Applying parallel training systems to complex models adds nontrivial development overheads in addition to model prototyping, and often results in lower-than-expected performance. This tutorial identifies research and practical pain points in parallel ML training, and discusses latest development of algorithms and systems on addressing these challenges in both usability and performance. In particular, this tutorial presents a new perspective of unifying seemingly different distributed ML training strategies. Based on it, introduces new techniques and system architectures to simplify and automate ML parallelization. This tutorial is built upon the authors' years' of research and industry experience, comprehensive literature survey, and several latest tutorials and papers published by the authors and peer researchers. The tutorial consists of four parts. The first part will present a landscape of distributed ML training techniques and systems, and highlight the major difficulties faced by real users when writing distributed ML code with big model or big data. The second part dives deep to explain the mainstream training strategies, guided with real use case. By developing a new and unified formulation to represent the seemingly different data- and model- parallel strategies, we describe a set of techniques and algorithms to achieve ML auto-parallelization, and compiler system architectures for auto-generating and exercising parallelization strategies based on models and clusters. The third part of this tutorial exposes a hidden layer of practical pain points in distributed ML training: hyper-parameter tuning and resource allocation, and introduces techniques to improve these aspects. The fourth part is designed as a hands-on coding session, in which we will walk through the audiences on writing distributed training programs in Python, using the various distributed ML tools and interfaces provided by the Ray ecosystem. 
    more » « less
  5. s research brief will share insights from using the curriculum, Thinking With Algebra (TWA), a National Science Foundation Project (DUE 2021414). The focus will be on the value of using this curriculum when working specifically with students who have taken Algebra I and Algebra II in high school, but were not prepared for college algebra or precalculus. This curriculum is specifically designed for STEM-track students who place below the level of college algebra when they enter college. The authors have found that TWA helps instructors honor the mathematical knowledge and ideas that students bring into the classroom. The curriculum units give instructors the freedom to choose the topics that suit their objectives. TWA also offers a variety of approaches to help students connect with mathematics in a new way. This brief will expand on these them students that the authors observed when teaching with this curriculum. 
    more » « less