Assessing the trustworthiness of information online is complicated. Literacy-based paradigms are both widely used to help and widely critiqued. We conducted a study with 35 Gen Zers from across the U.S. to understand how they assess information online. We found that they tended to encounter—rather than search for—information, and that those encounters were shaped more by social motivations than by truth-seeking queries. For them, information processing is fundamentally a social practice. Gen Zers interpreted online information together, as aspirational members of social groups. Our participants sought information sensibility: a socially-informed awareness of the value of information encountered online. We outline key challenges they faced and practices they used to make sense of information. Our findings suggest that like their information sensibility practices, solutions and strategies to address misinformation should be embedded in social contexts online.

While modern information visualization (IV) has been around for several decades, the inventions of IV seem to be peripheral to the everyday work in companies that would seem to be the most likely to use these inventions. In this case study, Google uses very few IV tools, relying mostly on more traditional ways of looking at data and data relationships. What has brought about this state of affairs? An analysis shows that the basic causes of low adoption are (a) difficulty of data wrangling and sharing the work products of analysis, (b) the need to share a common visual language literacy across different parts of the organization, (c) problems in using IV tools to communicate and present complex data analyses. At the same time, IV technology is found to be more useful in the investigation phase of research, rather than for communication and presentation reasons.

research-article Share on Introduction to Online Learning at Scale Editors: Benjamin B. Bederson View Profile , Daniel M. Russell View Profile , Scott Klemmer View Profile Authors Info & Claims ACM Transactions on Computer-Human InteractionVolume 22Issue 2April 2015 Article No.: 5pp 1–3https://doi.org/10.1145/2737794Published:15 April 2015Publication History 6citation477DownloadsMetricsTotal Citations6Total Downloads477Last 12 Months28Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Students who registered for the Mapping with Google massive open online course (MOOC) were asked several questions during the registration process to identify prior experience with eleven skills as well as their goals for registering for the course. Students selected goals from a list; they were periodically reminded of these goals during the MOOC. At the end of the course, we compared students' self reports of goal achievement on a post-course survey with behavioral click-stream analysis. In addition, we assessed how well prior skill in a subject predicts a student's course completion and found no correlation. Our research shows that students who completed course activities were more likely to earn certificates of completion than peers who did not.

While there is a large amount of work on creating autograded massive open online courses (MOOCs), some kinds of complex, qualitative exam questions are still beyond the current state of the art. For MOOCs that need to deal with these kinds of questions, it is not possible for a small course staff to grade students' qualitative work. To test the efficacy of self-evaluation as a method for complex-question evaluation, students in two Google MOOCs have submitted projects and evaluated their own work. For both courses, teaching assistants graded a random sample of papers and compared their grades with self-evaluated student grades. We found that many of the submitted projects were of very high quality, and that a large majority of self-evaluated projects were accurately evaluated, scoring within just a few points of the gold standard grading.

Web search performance can be improved by either improving the search engine itself or by educating the user to search more efficiently. There is a large amount of literature describing techniques for measuring the former; whereas, improvements resulting from the latter are more difficult to quantify. In this paper we demonstrate an experimental methodology that proves to successfully quantify improvements from user education. The user education in our study is realized in the form of tactical search feature tips that expand user awareness of task-relevant tools and features of the search application. Initially, these tips are presented in an idealized situation: each tip is shown at the same time as the study participants are given a task that is constructed to benefit from the specific tip. However, we also present a follow-up study roughly one week later in which the search tips are no longer presented but the study participants who previously were shown search tips still demonstrate improved search efficiency compared to the control group. This research has implications for search user interface designers and the study of information retrieval systems.

Making sense of the world using information technology has become a ubiquitous activity in the digital era. Sensemaking, as in to make sense, suggests an active processing of information to achieve...

The overwhelming amount of information on the web makes it critical for users to quickly and accurately evaluate the relevance of content. Here we tested whether pupil size can be used to discriminate the perceived relevance of web search results. Our findings revealed that measures of pupil size carry information that can be used to discriminate the relevance of text and image web search results, but the low signal-to-noise ratio poses challenges that need to be overcome when using this technique in naturalistic settings. Despite these challenges, our findings highlight the promise that pupillometry has as a technique that can be used to assess interest and relevance in web interaction in a non-intrusive and objective way.

Instant messaging (IM) in the workplace has proven to be a valuable tool for facilitating informal communication. Its benefits, however, are generally limited to times when users are in front of their computers. Because so much work takes place while people are mobile within their workplace, we sought to extend the benefits of IM beyond people's personal machines and into publicly accessible groupware. We first conducted a study of large display groupware applications (LDGAs) to understand the affordances that large displays offer for groupware, and the factors surrounding their adoption. We developed the IM Here system for shared IM on large displays using the lessons learned from the study. In this paper, we present the findings of our LDGA study, the design of IM Here and the preliminary results of our evaluation of IM as a public resource for workgroups.

Current information retrieval interfaces only address a small pant of the reality of rich interactions amongst user, task, and information sources.We view information gathering as an interactive, iterative activity involving multiple disparate information sources and embedded in the context of broader processes of information use.We have developed two key system components that enable information workspaces that adhere to this reformulation of information retrieval.The tlrst is a design for a user/system interaction model for retrieval from multiple, disparate information sources.The second is a repository modeling system, called Repo, that represents meta-information about different information repositories in a manner that supports system operation as well as provides a direct information resource to the user.To test these embodied the interaction system called Labrador.

To meet the challenge of constructing interfaces for increasingly complex multifunctional products, designers will be attracted by the promise offered by “intelligent” systems. However, the value of such sophisticated systems must be measured in terms of the quality of their user's models. One such intelligent interface — an Expert Help System — has been designed, implemented, and evaluated. We argue that the operability problems noted in users' interactions with this system are attributable to lack of a strong user model in the system interface. Such a model plays a critical role in determining the effectiveness of the system's ability to monitor the user's planning activities. We discuss the requirements of a strong user model and provide an example of how such a model might be integrated into a planner-based intelligent interface.