Certain video games show promise as tools for training spatial skills, one of the strongest predictors of future success in STEM. However, little is known about the gaming preferences of those who would benefit the most from such interventions: low spatial skill students. To provide guidance on how to design training games for this population, we conducted a survey of 350 participants from three populations: online college-age, students from a low SES high school, and students from a high SES high school. Participants took a timed test of spatial skills and then answered questions about their demographics, gameplay habits, preferences, and motivations. The only predictors of spatial skill were gender and population: female participants from online and low SES high school populations had the lowest spatial skill. In light of these findings, we provide design recommendations for game-based spatial skill interventions targeting low spatial skill students.

We are building an intelligent agent to help teaming efforts. In this paper, we investigate the real-world use of such an agent to understand students deeply and help student team formation in a large university class involving about 200 students and 40 teams. Specifically, the agent interacted with each student in a text-based conversation at the beginning and end of the class. We show how the intelligent agent was able to elicit in-depth information from the students, infer the students' personality traits, and reveal the complex relationships between team personality compositions and team results. We also report on the students' behavior with and impression of the agent. We discuss the benefits and limitations of such an intelligent agent in helping team formation, and the design considerations for creating intelligent agents for aiding in teaming efforts.

Mediation analysis is commonly performed using regressions or Bayesian network analysis in statistics, psychology, and health science; however, it is not effectively supported in existing visualization tools. The lack of assistance poses great risks when people use visualizations to explore causal relationships and make data-driven decisions, as spurious correlations or seemingly conflicting visual patterns might occur. In this paper, we focused on the causal reasoning task over three variables and investigated how an interface could help users reason more efficiently. We developed an interface that facilitates two processes involved in causal reasoning: 1) detecting inconsistent trends, which guides users' attention to important visual evidence, and 2) interpreting visualizations, by providing assisting visual cues and allowing users to compare key visualizations side by side. Our preliminary study showed that the features are potentially beneficial. We discuss design implications and how the features could be generalized for more complex causal analysis.

Digital gifting in live streaming, in which viewers buy digital gifts to reward the streamers, was worth over $200 million in 2018 in China and its growth has been accelerating. This paper explores what motivates people to tip and how it impacts interactions between viewers and streamers. Through a survey, we identified the main categories of viewers' tipping motivations. We found that viewers were motivated by the reciprocal acts of streamers, who would engage in various types of social interactions with tippers during the live streams. The styles of interactions and contents of live stream based on the tipping are differently influenced by the motivations of viewers and streamers. For example, viewers often tip large to attract attentions from the crowd or promote preferred live-streaming content. These findings provide more knowledge on the social interaction in live streaming platforms.

Abstract Interactive visualization tools are being used by an increasing number of members of the general public; however, little is known about how, and how well, people use visualizations to infer causality. Adapted from the mediation causal model, we designed an analytic framework to systematically evaluate human performance, strategies, and pitfalls in a visual causal reasoning task. We recruited 24 participants and asked them to identify the mediators in a fictitious dataset using bar charts and scatter plots within our visualization interface. The results showed that the accuracy of their responses as to whether a variable is a mediator significantly decreased when a confounding variable directly influenced the variable being analyzed. Further analysis demonstrated how individual visualization exploration strategies and interfaces might influence reasoning performance. We also identified common strategies and pitfalls in their causal reasoning processes. Design implications for how future visual analytics tools can be designed to better support causal inference are discussed.

Videos are essential for successful crowdfunding campaigns. However, without knowledge of the underlying persuasion factors, novice entrepreneurs may find it difficult to optimize their videos for success. This paper presents VidLyz, a novel assistive tool that allows users to explore the implications of audience-engagement persuasion factors in the context of campaign videos through contrasting examples, crowd-sourced subjective ratings, and feedback on engagement factors. VidLyz promotes active thinking about the impact of audience persuasion factors in making an effective campaign video by guiding novice entrepreneurs in planning materials of their videos on their own, with consideration of the product category, the target audience, and audience-product interactions. To evaluate our system, we collected subjective ratings and feedback on persuasion factors for 140 Kickstarter campaign videos from 2100 crowd workers and presented them through our prototype VidLyz tool. A user study with 45 novice users and five previous campaign creators found that our tool was useful for understanding the implication and relative importance of the persuasion factors of the campaign videos. The interactive and active thinking elements of VidLyz promoted novice users to make coherent and persuasive pre-production plan (using storyboards) for their proposed campaign videos. A follow-up user study showed that these storyboards had a higher likelihood of getting funded by crowd workers than those with less persuasive pre-production plan. We concluded with design implications to better support novice entrepreneurs.

Live streaming is an emergent social medium that allows remote interaction between the streamer and an audience of any size. Major live-streaming platforms in some Asian countries have a digital gift-giving feature that allows viewers to directly reward streamers during live sessions. Streamers can later exchange the digital gifts they have received for money, and this monetary incentive appears likely to influence how they interact with their viewers and generate live-streaming content. However, the precise nature and mechanisms of such impact have not previously been explored. Therefore, this qualitative study with 13 streamer participants examines how digital gifting influences streamers' motivations and the nature of both the content that they generate and their social interactions with their audiences. It reports that the digital-gifting function serves as a major incentive for active streaming, but may also disincentivize some streamers from continuing to contribute, for reasons that will be explored. Moreover, the participants devised strategies for both content generation and social interaction with the specific objective of earning gifts from viewers: practices that, in some cases, appeared to limit the quality of their live-streaming content. It was also noted that the streamers tended to have constrained social relationships with their viewers, in part because such relationships were seen as unequal or one-sided due to gift-giving behavior. The paper concludes with a discussion of design considerations for the incorporation of gift-giving features into live-streaming platforms, and additional recommendations for future research and the design of such platforms.

This special issue highlights 11 papers from the ACM Intelligent User Interface (IUI) 2017 international conference, which received 272 submissions and of which 63 of them were accepted by the program committee (acceptance rate: 23%).The 11 articles in this special issue of TiiS were selected by the program committee to be among the best papers accepted into the program of ACM IUI 2017.Each of these selected conference papers was extended, with at least 30% new materials in the new version, which allowed authors to elaborate on their excellent lines of research relevant to the ACM IUI community.The extended versions of the papers have gone through a new review process by at least three reviewers.This special issue starts with two articles on analyzing and learning from multimodal signals to inform human behavior.In "AttentiveVideo: A Multimodal Approach to Quantify Emotional Responses to Mobile Advertisements," the authors propose AttentiveVideo, a scalable intelligent mobile interface with corresponding inference algorithms to monitor and quantify the effects of mobile video advertising in real time.This interface employs a combination of implicit photoplethysmography sensing and facial expression analysis to detect attention, engagement, and sentiment of viewers as they watch video advertisements on unmodified smartphones.In "Wearables and Social Signal Processing for Better Public Presentations," the authors studied

Donation-based crowdfunding platforms have an increasing number of campaigns on socially stigmatized topics. These platforms' widespread online reachability and the large flow of monetary donations have the potential to shape individuals' opinions by influencing their perceptions. However, little research has been done to investigate whether these campaigns impact individuals' opinions and how. We conducted an experiment to explore how an attitude-inconsistent campaign on fairness and equality for LGBTIQ people influenced participants' opinion on this topic. Although all the participants changed their perceived opinions after reading the support for the campaigns, participants opposing equality were less inclined to change their attitude than participants supporting equality. To examine this difference further, we conducted another experiment where participants were exposed to both attitude-consistent and attitude-inconsistent campaigns with varying levels of social support. Participants opposing equality showed less sensitivity to the level of social support, and wanted to donate significantly more money to anti-equality campaigns compared to those who supported equality. Results demonstrate the complex role of crowdfunding campaigns in shaping individuals' opinions on stigmatized topics.

Prior research has shown that spatial abilities are crucial for STEM achievement and attainment. The connection between the digital and physical worlds provided by embodied interaction has been shown to enhance performance and engagement in educational contexts. Spatial reasoning is a domain that lends itself naturally to embodied, physical interaction; however, there is little understanding of how embodied interaction could be incorporated into educational technology designed to train spatial reasoning skills. We propose several guidelines for gestural interaction design in spatial reasoning education games based on an empirical study with students at a local afterschool program using a custom-built computer game for training spatial skills. We present a series of gesture sets derived from an iterative design approach that are easy for children to acquire, show sufficient congruency to specific spatial operations, and enable robust recognition from the system. We also compared children's behaviors when playing the game with our gestural interface and a traditional mouse-based interface and found that children take more time but fewer steps to complete game levels when using gestures.

Social media users are overloaded with diverse opinions by people with opposing stances. Previous research shows that people often look for opinions that reinforce their pre-existing beliefs and stances, which may lead to social polarization. Traditional social media present opinions in a linear list format, which not only lacks structures for people to explore diverse viewpoints but also aggravates their selective exposure to agreeable opinions. To address this problem, we designed an intelligent system that improves awareness of diverse social opinions by providing visual hints and recommendations of opinions (e.g. news articles and comments) on different sides with different indicators. We evaluated our system with news articles about Obamacare repeal issue and their corresponding user comments from Facebook. Results demonstrate that our system could increase people»s awareness of their stances and opinion selection preferences, which mitigates selective exposure and thereby leads to a more balanced perception of social opinions.

Research has demonstrated that spatial visualization skills are crucial for success in Science, technology, engineering, and mathematics (STEM) disciplines. With an increasing number of students entering STEM disciplines, the question of how to effectively train students» spatial visualization skills has become very important. While a scalable existing solution is to implement online workshops for students, the problem of how to motivate students to participate in these online workshops remains unsolved. In this study, we studied gamification as a way to motivate first year engineering students to take part in an online workshop designed to train their spatial visualization skills. Our game contains eight modules, each designed to train a different component of spatial visualization. The game records players» in-game behavior with high granularity, which allows us to provide automated, scalable feedback on players» problem-solving strategies. Ten students with different levels of spatial ability played our game and expressed a strong interest in using the game to train their spatial visualization skills in the future. In addition, our analysis of players» in-game behaviors shows the potential benefits of implementing adaptive and personalized learning guidance.

On existing crowdfunding platforms, the allocation of money is often not regulated, which leads to less-than-ideal distribution of resources. For example, recent donations to hurricane victims through their crowdfunding campaigns often lead to overfunding of certain victims while underfunding others. Inspired by algorithms from economic theories, we proposed a Smart Crowd Donate system encourages donors to express preferences to multiple projects and reallocates funds dynamically across these preferences over time. We conducted a user study in which recruited 452 participants to simulate a small scale of crowdfunding. The findings of our user study supported the idea that the Smart Crowd Donate system has potential to efficiently distribute funds to projects and allows more projects to receive the amount of money they need.

Augmented Reality (AR) allows virtual object projection with an unblocked view of the physical world which provides reference and other people. The mixed scene provides an agile platform for communication and collaboration, especially on a product that would be difficult or expensive to present otherwise. In the boating industry, high customization leaves dealers with a high cost on inventory, financially and spatially. In this work, we present BoatAR, a multi-user AR boat configuration system designed for addressing these issues. A prototype system was implemented using HoloLens with shared experience, and demonstrated to a group of boat dealers and received positive feedback. BoatAR provided an example of how a multi-user AR system could help in the conventional industry.

Social media sites use different labels to help users find and select news feeds. For example, Blue Feed, Red Feed, a news feed created by the Wall Street Journal, use stance labels to separate news articles with opposing political ideologies to help people explore diverse opinions. To combat the spread of fake news, Facebook has experimented with putting credibility labels on news articles to help readers decide whether the content is trustworthy. To systematically understand the effects of stance and credibility labels on online news selection and consumption, we conducted a controlled experiment to study how these labels influence the selection, perceived extremeness, and level of agreement of news articles. Results show that stance labels may intensify selective exposure - a tendency for people to look for agreeable opinions -- and make people more vulnerable to polarized opinions and fake news. We found, however, that the effect of credibility labels on reducing selective exposure and recognizing fake news is limited. Although originally designed to encourage exposure to opposite viewpoints, stance labels can make fake news articles look more trustworthy, and they may lower people's perception of the extremeness of fake news articles. Our results have important implications on the subtle effects of stance and credibility labels on online news consumption.

Understanding the factors that persuade backers to donate to research projects has become increasingly important with the rising popularity of scientific crowdfunding. Although there are many similarities between enterprise and scientific crowdfunding, some factors differentiate these two forms of crowdfunding. One such factor is the use of endorsements. The endorsement helps backers gain trust based on expert opinions about the competency of the researchers and the usefulness of the projects. We analyzed 810 endorsements from scientific campaigns posted on Experiment.com and derived a taxonomy of topics discussed in the endorsements. A regression analysis revealed that when endorsers explained the skills of the campaign owners, the probability of success of the campaign improved; on the contrary, when endorsers reiterated the goal of the project, the campaign was less likely to succeed. We conclude with design implications formulated from our findings to better support scientific crowdfunding.

Instructors are increasingly using algorithmic tools for team formation, yet little is known about how these tools are applied or how students and instructors perceive their use. We studied a representative team formation tool (CATME) in eight project-based courses. An instructor uses the tool to form teams by surveying students' working styles, skills, and demographics; then configuring these criteria as input into an algorithm that assigns teams. We surveyed students (N=277) in the courses to gauge their perceptions of the strengths and weaknesses of the tool and ideas for improving it. We also interviewed instructors (N=13) different from those who taught the eight courses to learn about their criteria selections and perceptions of the tool. Students valued the rational basis for forming teams but desired a stronger voice in criteria selection and explanations as to why they were assigned to a particular team. Instructors appreciated the efficiency of team formation but wanted to view exemplars of criteria used in similar courses. This work contributes recommendations for deploying team formation tools in educational settings and for better satisfying the goals of all stakeholders.

As demand for design education increases, instructors are struggling to provide timely, personalized feedback for student projects. Gathering feedback from classroom peers and external crowds offer scalable approaches, but there is little evidence of how they compare. We report on a study in which students (n=127) created early- and late-stage prototypes as part of nine-week projects. At each stage, students received feedback from peers and external crowds: their own social networks, online communities, and a task market. We measured the quality, quantity and valence of the feedback and the actions taken on it, and categorized its content using a taxonomy of critique discourse. The study found that peers produced feedback that was of higher perceived quality, acted upon more, and longer compared to the crowds. However, crowd feedback was found to be a viable supplement to peer feedback and students preferred it for projects targeting specialized audiences. Feedback from all sources spanned only a subset of the critique categories. Instructors may fill this gap by further scaffolding feedback generation. The study contributes insights for how to best utilize different feedback sources in project-based courses.

This study investigated the effects of age, prior domain knowledge and prior knowledge semantic pre-activation task on information searching. Results showed that the age-related differences were observed for the fantastic movies domain (for which older adults had significantly lower prior knowledge), whereas the effects of age were not replicated in the health domain (for which older adults had equal knowledge with young adults?). Semantic pre-activation of prior knowledge in the health domain was correlated to more analytic strategies when processing the search engine pages and when navigating.

To successfully raise money using crowdfunding, it is important for a campaign to communicate ideas or products effectively to the potential backers. One of the lesser explored but powerful components of a crowdfunding campaign is the campaign video. To better understand how videos affect campaign outcomes, we analyzed videos from 210 Kickstarter campaigns across three different project categories. In a mixed-methods study, we asked 3150 Amazon Mechanical Turk (MTurk) workers to evaluate the campaign videos. We found six recurrent factors from a qualitative analysis as well as quantitative analysis. Analysis revealed product related and video related factors that were predictive of the final outcome of campaigns over and above the static project representation features identified in previous studies. Both the qualitative and quantitative analysis showed that videos influenced perception differently for projects in different categories, and the differential perception was important for predicting successes of the projects. For example, in technology campaigns, projects perceived to have a lower level of complexity were more likely to be successful; but in design and fashion campaigns, projects perceived to have a higher level of complexity - which perhaps reflected craftsmanship - were more likely to be successful. We conclude with design implications to better support the video making process.

An enormous amount of posts and comments are shared in online social forums, which often organize these online social opinions based on semantic contents. However, for controversial topics, people with different attitudes and stances often have very distinct perspectives, reactions, and emotions to the same post. Organization by semantic contents often encourages selective exposure to information, which may exacerbate opinion polarization. To address this problem, we design a novel interface that allows people to better understand and appreciate people with different stances in social forums. Our interface was developed to allow interactive visualization and categorization of original posts about a controversial topic with crowd workers' reactions and emotions from different stances. We evaluated the interface using Reddit posts about US presidential candidates. Results demonstrate that the interface can mitigate selective exposure and help users to adopt a broader spectrum of opinions than the traditional Reddit interface.

Hint systems are designed to adjust a video game's difficulty to suit the individual player, but too often they are designed without analyzing player behavior and lack intelligence and adaptability, resulting in hints that are at best ineffective and at worst hurt player experience. We present an alternative approach to hint design focusing on player experience rather than performance. We had 25 participants play a difficult spatial puzzle game and collected player behavior, demographics, and self-reported player experience measures. We found that more exploratory behavior improved player experience, so we designed three types of hints encouraging this behavior: adaptive, automatic, and on-demand. We found that certain players found hints more helpful regardless of whether the hints changed their behavior, and players seemed to prefer seeing fewer hints than the adaptive and automatic conditions gave them. Our findings contribute a deeper empirical understanding of hint design strategies and their effect on player behavior and experience, with practical recommendations for designers of interactive systems.

Certain commercial video games, such as Portal 2 and Tetris, have been empirically shown to train spatial reasoning skills, a subset of cognitive skills essential for success in STEM disciplines. However, no research to date has attempted to understand which specific features in these games tap into players' spatial ability or how individual player differences interact with these game features. This knowledge is crucially important as a first step towards understanding what makes these games effective and why, especially for subpopulations with lower spatial ability such as women and girls. We present the first empirical study analyzing the relationship between spatial ability, specific game features, and individual player differences using a custom-built computer game. Twenty children took a pretest of spatial skills and then played our game for 2 hours. We found that spatial ability pretest scores predicted several player behaviors related to in-game tasks involving 3D object construction and first person navigation. However, when analyzed by gender, girls' pretest scores were much less predictive of player behavior.

In this study, we evaluated the ability of computational cognitive models of web‐navigation like CoLiDeS and CoLiDeS+ to model i) user interactions with search engines and ii) individual differences in search behavior due to variations in cognitive factors such as aging. CoLiDeS and CoLiDeS+ were extended to predict user clicks on search engine result pages. Their performance was evaluated using actual behavioral data from an experiment in which 2 types of information search tasks (simple vs. difficult), were presented to younger and older participants. The results showed that the model predictions matched significantly better with the actual user behavior on difficult tasks compared to simple tasks and with younger participants compared to older participants, especially for difficult tasks. Also, the matches were significantly better with CoLiDeS+ compared to CoLiDeS, especially for difficult tasks. We conclude that the advanced capabilities of CoLiDeS+, such as incorporating contextual information and implementing backtracking strategies enable it to predict user behavior significantly better than CoLiDeS, especially on difficult tasks. The usefulness of these modeling outcomes for the design of support systems for older adults is discussed.

Traditional medical fundraising charities have been relying on third-party watchdogs and carefully crafting their reputation over time to signal their credibility to potential donors. As medical fundraising campaigns migrate to online platforms in the form of crowdfunding, potential donors can no longer rely on the organization's traditional methods for achieving credibility. Individual fundraisers must establish credibility on their own. Potential donors, therefore, seek new factors to assess the credibility of crowdfunding campaigns. In this paper, we investigate current practices in assessing the credibility of online medical crowdfunding campaigns. We report results from a mixed-methods study that analyzed data from social media and semi-structured interviews. We discovered eleven factors associated with the perceived credibility of medical crowdfunding. Of these, three communicative/emotional factors were unique to medical crowdfunding. We also found a distinctive validation practice, the collective endorsement. Close-connections' online presence and external online communities come together to form this collective endorsement in online medical fundraising campaigns. We conclude by describing how fundraisers can leverage collective endorsements to improve their campaigns' perceived credibility.

We present a study of 10-month Twitter discussions on the controversial topic of Edward Snowden. We demonstrate how behavioral differences of opinion groups can distort the presence of opinions on a social media platform. By studying the differences between a numerical minority (anti-Snowden) and a majority (pro-Snowden) group, we found that the minority group engaged in a "shared audiencing" practice with more persistent production of original tweets, focusing increasingly on inter-personal interactions with like-minded others. The majority group engaged in a "gatewatching" practice by disseminating information from the group, and over time shifted further from making original comments to retweeting others'. The findings show consistency with previous social science research on how social environment shapes majority and minority group behaviors. We also highlight that they can be further distorted by the collective use of social media design features such as the "retweet" button, by introducing the concept of "amplification'" to measure how a design feature biases the voice of an opinion group. Our work presents a warning to not oversimplify analysis of social media data for inferring social opinions.

The process of generating schematic maps of salient objects from a set of pictures of an indoor environment is challenging. It has been an active area of research as it is crucial to a wide range of context- and location-aware services, as well as for general scene understanding. Although many automated systems have been developed to solve the problem, most of them either require predefining labels or expensive equipment, such as RGBD sensors or lasers, to scan the environment. In this article, we introduce a prototype system to show how human computations can be utilized to generate schematic maps from a set of pictures, without making strong assumptions or demanding extra devices. The system requires humans (crowd workers from Amazon Mechanical Turks) to do simple spatial mapping tasks in various conditions, and their data are aggregated by filtering and clustering techniques that allow salient cues to be identified in the pictures and their spatial relations to be inferred and projected on a two-dimensional map. In particular, we tested and demonstrated the effectiveness of two methods that improved the quality of the generated schematic map: (1) We encouraged humans to adopt an allocentric representations of salient objects by guiding them to perform mental rotations of these objects and (2) we sensitized human perception by guided arrows superimposed on the imagery to improve the accuracy of depth and width estimation. We demonstrated the feasibility of our system by evaluating the results of schematic maps generated from indoor pictures taken from an office building. By calculating Riemannian shape distances between the generated maps to the ground truth, we found that the generated schematic maps captured the spatial relations well. Our results showed that the combination of human computations and machine clustering could lead to more-accurate schematized maps from imagery. We also discuss how our approach may have important insights on methods that leverage human computations in other areas.

Selective exposure, the preferential seeking of confirmatory information, can potentially exacerbate fragmentation of online opinions and lead to biased decisions. We tested whether features that allowed users to better distinguish information about different issue aspects would encourage them to take different perspectives, thereby moderating the negative influence of pre-existing beliefs on information seeking. Using an information aggregator that provided drug related comments, we conducted an experiment to study the impact of aspect indicators (indicating whether the comment was about effectiveness or side effects) on moderating selective exposure. We found that, when participants were asked to decide between medications for high-risk diseases, and had preexisting biased beliefs in their effectiveness (one medication was less effective than the other), without aspect indicators they exhibited selective exposure to both types of comments (effectiveness and side effects) and were biased to choose the medication in confirmation of their pre-existing beliefs. With aspect indicators, we found reduced selective exposure to information about side effects of the medications, and as a result their overall decision bias was mitigated. However, the effect of aspect indicators in reducing selective exposure was moderated by the decision contexts, including the perceived risk of the diseases and whether the aspect was perceived to be critical to the decision.

Increasing number of older adults manage their retirement savings online. A crucial element of better management is to take rational financial risk -- to strike a reasonable balance between expected gain and loss under uncertainty. With the emergence of Web 2.0 technologies, social trading networks can help individuals make better financial decisions by providing information about others' actions. It is, however, unclear whether these resources is beneficial to older adult's own financial decisions, especially because older adults are vulnerable to poor risk management. To address this question, we devise an experiment that improves upon an existing experimental economic task. We find that both peer information (detailed choices by a few individuals) and majority information (aggregated choices of the crowd) help older adults make more risk-neutral decisions. Furthermore, the combination of peer and majority information corrects more mistakes of more risk-averse older adults.

We studied how a source expertise indicator impacted users' information seeking behavior when using a system aggregating diverse opinions, and how it interacted with a source position indicator to shape users' selectivity of information. We found that, for both attitude consistent and inconsistent information, the expertise indicator increased the selection of sources indicated to have high expertise and decreased that of low expertise. Moreover, when both source expertise and position indicators were present, users' selective exposure tendency, i.e., preferential selection of attitude consistent sources over inconsistent ones, decreased among expert sources. Moreover, we found that the expertise indicator could benefit encouraging common ground seeking with different others by increasing the agreement with, and perceived expertise of inconsistent sources indicated to be experts. Design implications for moderating selective exposure by highlighting the utility of dissonant information were discussed.

Hundreds of thousands of crowdfunding campaigns have been launched, but more than half of them have failed. To better understand the factors affecting campaign outcomes, this paper targets the content and usage patterns of project updates -- communications intended to keep potential funders aware of a campaign's progress. We analyzed the content and usage patterns of a large corpus of project updates on Kickstarter, one of the largest crowdfunding platforms. Using semantic analysis techniques, we derived a taxonomy of the types of project updates created during campaigns, and found discrepancies between the design intent of a project update and the various uses in practice (e.g. social promotion). The analysis also showed that specific uses of updates had stronger associations with campaign success than the project's description. Design implications were formulated from the results to help designers better support various uses of updates in crowdfunding campaigns.

We examined how a source position indicator showing both valences (pro/con) and magnitudes (moderate/extreme) of positions on controversial topics influenced users' selection and reception of diverse opinions in online discussions. Results showed that the indicator had differential impact on participants who had varied levels of accuracy motives -- i.e., motivation to accurately learn about the topic, by leading to greater exposure to attitude-challenging information for participants with higher accuracy motives. Further analysis revealed that it was mainly caused by the fact that the presence of position indicator increased the selection of moderately inconsistent sources for participants with high accuracy motives but decreased the selection of them for participants with low accuracy motives. The indicator also helped participants differentiate between sources with moderate and extreme positions, and increased their tendency to agree with attitude-challenging information from sources with moderately inconsistent positions. Participants with high accuracy motives were also found to learn significantly more about the arguments put forward by the opposite side with the help of the position indicator. We discussed the implications of the results for the nature of the echo chamber effect, as well as for designing information systems that encourage seeking of diverse information and common ground seeking.

Geospatial data is becoming increasingly important for many uses, from expert scientific domains to social media interaction. A lot of this data is three-dimensional in nature and in recent years virtual globe software like Google Earth has made visualising such data much easier. However there has not been an equivalent improvement in the way in which users interact with the visualisations. Recognising that navigating a virtual world encompassing data at scales across many orders of magnitude requires a 7-dimensional representation of the view state, we have implemented our AeroSpace 7DoF interaction method [Stannus et al. 2014] in a Geovisualisation testbed built around NASA's World Wind virtual globe software. Our approach expands the two-point touch method [Hancock et al.] to 3D with pinches replacing touches. It surpasses previous two-point work [Wang et al. 2011] [Song et al.] [Schultheis et al. 2012] by allowing direct, bimanual, integral, simultaneous navigation with index-finger-moderated roll around the ambiguous axis, though the virtual hands were offset indirectly to mitigate occlusion. Our system was implemented in an immersive environment with 3 large stereo screens for display and an ARTrack3 system for tracking the users head (via retroreflective markers on stereoscopic glasses) and hands. Custom gloves were used to report pinch and pointing gesture contact states via Bluetooth and provide 5DoF tracking via markers on the index fingers.

Older adults are a notable group among the exponentially growing population of online health information consumers. In order to better support older adults’ health-related information seeking on the Internet, it is important to understand how they judge the credibility of such information when compared to younger users. We conducted two laboratory studies to explore how the credibility cues in message contents, website features, and user-generated comments differentially impact younger (19 to 26 years of age) and older adults’ (58 to 80 years of age) credibility judgments. Results from the first experiment showed that older adults were less sensitive to the credibility cues in message contents and those in website features than younger adults. Verbal protocol analysis revealed that these differences could be caused by the higher tendency of older adults to passively accept web information, and their lack of deliberation on its quality and attention towards contextual web features (e.g., design look, source identity). In the second experiment, we studied how credibility cues from user reviews might differentially impact older and younger adults’ credibility judgments of online health information. Results showed that consistent credibility cues in user reviews and message contents could facilitate older adults’ credibility judgments. When the two were inconsistent, older adults, as compared to younger ones, were less swayed by highly appraising user reviews given to low credibility information. These results provided important implications for designing health information technologies that better fit the older population.

This paper studied how social transparency and different peer-dependent reward schemes (i.e., individual, teamwork, and competition) affect the outcomes of crowdsourcing. The results showed that when social transparency was increased by asking otherwise anonymous workers to share their demographic information (e.g., name, nationality) to the paired worker, they performed significantly better. A more detailed analysis showed that in a teamwork reward scheme, in which the reward of the paired workers depended only on the collective outcomes, increasing social transparency could offset effects of social loafing by making them more accountable to their teammates. In a competition reward scheme, in which workers competed against each other and the reward depended on how much they outperformed their opponent, increasing social transparency could augment effects of social facilitation by providing more incentives for them to outperform their opponent. The results suggested that a careful combination of methods that increase social transparency and different reward schemes can significantly improve crowdsourcing outcomes.

We investigated participants' preferential selection of information and their attitude moderation in an online environment. Results showed that even when opposing views were presented side-to-side, people would still preferentially select information that reinforced their existing attitudes. Preferential selection of information was, however, influenced by both situational (e.g., perceived threat) and personal (e.g., topic involvement) factors. Specifically, perceived threat induced selective exposure to attitude consistent information for topics that participants had low involvement. Participants had a higher tendency to select peer user opinions in topics that they had low than high involvement, but only when there was no perception of threat. Overall, participants' attitudes were moderated after being exposed to diverse views, although high topic involvement led to higher resistance to such moderation. Perceived threat also weakened attitude moderation, especially for low involvement topics. Results have important implication to the potential effects of "information bubble" - selective exposure can be induced by situational and personal factors even when competing views are presented side-by-side.

Peer consistency evaluation is often used in games with a purpose (GWAP) to evaluate workers using outputs of other workers without using gold standard answers. Despite its popularity, the reliability of peer consistency evaluation has never been systematically tested to show how it can be used as a general evaluation method in human computation systems. We present experimental results that show that human computation systems using peer consistency evaluation can lead to outcomes that are even better than those that evaluate workers using gold standard answers. We also show that even without evaluation, simply telling the workers that their answers will be used as future evaluation standards can significantly enhance the workers' performance. Results have important implication for methods that improve the reliability of human computation systems.

We created a spatial location identification task (SpLIT) in which workers recruited from Amazon Mechanical Turk were presented with a camera view of a location, and were asked to identify the location on a two-dimensional map. In cases where these cues were ambiguous or did not provide enough information to pinpoint the exact location, workers had to make a best guess. We tested the effects of two reward schemes. In the “ground truth” scheme, workers were rewarded if their answers were close enough to the correct locations. In the “majority vote” scheme, workers were told that they would be rewarded if their answers were similar to the majority of other workers. Results showed that the majority vote reward scheme led to consistently more accurate answers. Cluster analysis further showed that the majority vote reward scheme led to answers with higher reliability (a higher percentage of answers in the correct clusters) and precision (a smaller average distance to the cluster centers). Possible reasons for why the majority voting reward scheme was better were discussed.

We designed and tested the effects of an in-car augmented reality system (ARS) on younger and older drivers, with and without a secondary distraction task. When potential danger is detected, the ARS alerts the driver by progressively indicating the time to collision to the lead vehicle as well as merging vehicles from side lanes by an AR display that overlaps with the lead or merging vehicles. We tested the ARS with younger (18–30) and older (65–75) drivers in a high-fidelity driving simulator. Results showed that the ARS could significantly reduce collisions caused by hazard events such as sudden slowing of the lead vehicle or merging of vehicles from sides lanes. Consistent with previous results, older drivers, despite age-related decline in cognitive and motor abilities, could leverage their driving experience to avoid forward collisions with the lead vehicle as much as younger drivers. However, older drivers were poorer in avoiding collisions caused by sudden merging events than younger drivers. The ARS was found to be most useful in helping older adults to avoid collision caused by sudden hazard events, especially with the presence of a distraction task. The ARS was also more effective for older than younger drivers to encourage a safe driving distance with the lead vehicle. Interestingly, there seemed to be differential effects of the ARS on the general driving behavior of younger and older drivers. While older drivers in general became more careful and safer in how they drive with the ARS, younger drivers seemed to rely on the ARS to alert them to potential hazard events without adopting safer driving behavior.

Crowdsourcing and machine learning are both useful techniques for solving difficult problems (e.g., computer vision and natural language processing). In this paper, we propose a novel method that harnesses and combines the strength of these two techniques to better analyze the features and the sentiments toward them in user reviews. To strike a good balance between reducing information overload and providing the original context expressed by review writers, the proposed system (1) allows users to interactively rank the entities based on feature-rating, (2) automatically highlights sentences that are related to relevant features, and (3) utilizes implicit crowdsourcing by encouraging users to provide correct labels of their own reviews to improve the feature-sentiment classifier. The proposed system not only helps users to save time and effort to digest the often massive amount of user reviews, but also provides real-time suggestions on relevant features and ratings as users generate their own reviews. Results from a simulation experiment show that leveraging on the crowd can significantly improve the feature-sentiment analysis of user reviews. Furthermore, results from a user study show that the proposed interface was preferred by more participants than interfaces that use traditional noun-adjective pair summarization, as the current interface allows users to view feature-related information in the original context.

An empirical study was conducted to investigate how older and younger users learned by performing exploratory search of health information using an interface that recommended relevant links based on browsing histories. While older and younger users gained both factual and structural knowledge about the health topics, significant age differences were observed. Our results showed that processing of recommended and regular Web links imposed distinct demands on cognitive abilities, which at least partially explained the observed age differences in the search process. The use of recommended links was positively associated with general knowledge, while the use of regular Web links was positively associated with processing capacity. Results also showed that the recommended links benefited both younger and older adults by broadening the exploration of information, which led to better learning. Implications on designs of health information interfaces that facilitate exploratory search and learning for different age groups were discussed.

Judging topical expertise of micro-blogger is one of the key challenges for information seekers when deciding which information sources to follow. However, it is unclear how useful different types of information are for people to make expertise judgments and to what extent their background knowledge influences their judgments. This study explored differences between experts and novices in inferring expertise of Twitter users. In three conditions, participants rated the level of expertise of users after seeing (1) only the tweets, (2) only the contextual information including short biographical and user list information, and (3) both tweets and contextual information. Results indicated that, in general, contextual information provides more useful information for making expertise judgment of Twitter users than tweets. While the addition of tweets seems to make little difference, or even add nuances to novices' expertise judgment, experts' judgments were improved when both content and contextual information were presented.

We report results of a study which examines consensus building in user interface design discussions in open source software communities. Our methodology consisted of conducting interviews with designers and developers from the Drupal and Ubuntu communities (N=17) and analyzing a large corpus of interaction data collected from Drupal. The interviews captured user perspectives on the challenges of reaching consensus, techniques employed for building consensus, and the consequences of not reaching consensus. We analyzed the interaction data to determine how different elements of the content, process, and user relationships in the design discussions affect consensus. Our main result shows that design discussions engaging participants with more experience and prior interaction history are more likely to reach consensus. Based on all of our results, we formulated design implications for promoting consensus in distributed discussions of user interface design issues.

We compared the effects of three computer mediated communication (CMC) channels (text, audio, and video) on how people performed an appointment-scheduling task. The task involved a grounding and a conflict resolution component. The results showed that video conferencing supported participant dyads in reaching a consensus that had better balanced performance between the dyads only when task difficulty was high and when there were more inherent conflicts in the task. Participants across the three CMC conditions also demonstrated different patterns of conversation dynamics during information exchange and negotiation. Mediation analysis showed that in video-based communication, strategies of exchanging less information at a time predicted higher levels of negotiation, which in turn predicted smaller performance differences in high conflict conditions. The results suggested that the design and use of communication technologies for remote conflict resolution should promote the strategy of exchanging information in small pieces, which could better support subsequent negotiation and foster a sense of fairness.

We present results from an experiment that studied the information search behavior of younger and older adults in a medical decision-making task. To study how different combination of tasks and interfaces influenced search strategies and decision-making outcomes, we varied information structures of two interfaces and presented different task descriptions to participants. We found that younger adults tended to use different search strategies in different combination of tasks and interfaces, and older adults tended to use the same top-down strategies across conditions. We concluded that older adults were able to perform mental transformation of medical terms more effectively than younger adults. Thus older adults did not require changing strategies to maintain the same level of performance.

Do people from different cultures tag digital images differently? The current study compared the content of tags for digital images created by two cultural groups: European Americans and Chinese. In line with previous findings on cultural differences in attentional patterns, we found similar cultural differences in the order of the image parts (e.g., foreground or background objects) that people tag. We found that for European Americans, the first tag was more likely assigned to the main objects than that by Chinese; but for Chinese, the first tag was more likely assigned to the overall description or relations between objects in the images. The findings had significant implications for designing cultural-sensitive tools to facilitate the tagging and search process of digital media, as well as for developing data-mining tools that identify user profiles based on their tagging patterns and cultural origins.

Our study compared how experts and novices performed exploratory search using a traditional search engine and a social tagging system. As expected, results showed that social tagging systems could facilitate exploratory search for both experts and novices. We, however, also found that experts were better at interpreting the social tags and generating search keywords, which made them better at finding information in both interfaces. Specifically, experts found more general information than novices by better interpretation of social tags in the tagging system; and experts also found more domain-specific information by generating more of their own keywords. We found a dynamic interaction between knowledge-in-the-head and knowledge-in-the-social-web that although information seekers are more and more reliant on information from the social Web, domain expertise is still important in guiding them to find and evaluate the information. Implications on the design of social search systems that facilitate exploratory search are also discussed.

Do people from different cultures tag digital images differently? The current study examined the relationship between the position and content of tags for digital images created by participants from two cultural groups (European Americans and Chinese). In line with previous findings on cultural differences in attentional patterns, we found cultural differences in the order of the parts of images people chose to tag. European Americans tended to tag main objects first, and tag background objects and overall properties in the images later; in contrast, Chinese tended to tag the overall properties first, and tag the main and background objects later. Based on findings of the current study, we discuss implications on developing a cultural-sensitive algorithm to facilitate the tagging and search process of digital media and data-mining tools to identify user profiles based on their cultural origins.

We present an extension of a computational cognitive model of social tagging and exploratory search called the semantic imitation model. The model assumes a probabilistic representation of semantics for both internal and external knowledge, and utilizes social tags as navigational cues during exploratory search. We used the model to generate a measure of information scent that controls exploratory search behavior, and simulated the effects of multiple presentations of navigational cues on both simple information retrieval and exploratory search performance based on a previous model called SNIF-ACT. We found that search performance can be significantly improved by these model-based presentations of navigational cues for both experts and novices. The result suggested that exploratory search performance depends critically on the match between internal knowledge (domain expertise) and external knowledge structures (folksonomies). Results have significant implications on how social information systems should be designed to facilitate knowledge exchange among users with different background knowledge.

The arising popularity of social tagging system has the potential to transform traditional web search into a new era of social search. Based on the finding that domain expertise could influence search behavior in traditional search engines, we hypothesized and tested the idea that domain expertise would have similar influence on search behavior in a social tagging system. We conducted an experiment comparing search behavior of experts and novices when they searched using a tradition search engine and a social tagging system. Results from our experiment showed that experts relied more on their own domain knowledge to generate search queries, while novices were influenced more by social cues in the social tagging system. Experts were also found to conform to each other more than novices in their choice of bookmarks and tags. Implications on the design of future social information systems are discussed.

We present a semantic imitation model of social tagging and exploratory search based on theories of cognitive science. The model assumes that social tags evoke a spontaneous tag-based topic inference process that primes the semantic interpretation of resource contents during exploratory search, and the semantic priming of existing tags in turn influences future tag choices. The model predicts that (1) users who can see tags created by others tend to create tags that are semantically similar to these existing tags, demonstrating the social influence of tag choices; and (2) users who have similar information goals tend to create tags that are semantically similar, but this effect is mediated by the semantic representation and interpretation of social tags. Results from the experiment comparing tagging behavior between a social group (where participants can see tags created by others) and a nominal group (where participants cannot see tags created by others) confirmed these predictions. The current results highlight the critical role of human semantic representations and interpretation processes in the analysis of large-scale social information systems. The model implies that analysis at both the individual and social levels are important for understanding the active, dynamic processes between human knowledge structures and external folksonomies. Implications on how social tagging systems can facilitate exploratory search, interactive information retrievals, knowledge exchange, and other higher-level cognitive and learning activities are discussed.

Previous research has shown that older adults performed worse in web search tasks, and attributed poorer performance to a decline in their cognitive abilities. We conducted a study involving younger and older adults to compare their web search behavior and performance in ill-defined and well-defined information tasks using a health information website. In ill-defined tasks, only a general description about information needs was given, while in well-defined tasks, information needs as well as the specific target information were given. We found that older adults performed worse than younger adults in well-defined tasks, but the reverse was true in ill-defined tasks. Older adults compensated for their lower cognitive abilities by adopting a top-down knowledge-driven strategy to achieve the same level of performance in the ill-defined tasks. Indeed, path models showed that cognitive abilities, health literacy, and knowledge influenced search strategies adopted by older and younger adults. Design implications are also discussed.

This article presents a rational model developed under the distributed cognition framework that explains how social tags influence knowledge acquisition and adaptation in exploratory ill-defined information tasks. The model provides integrated predictions on the interactions among link selections, use and creation of tags, and the formation of mental categories. The model shows that the quality of tags not only influences search efficiency, but also the quality of mental categories formed during exploratory search. In addition, the model shows that aggregate regularities can be explained by microstructures of behavior that emerged from the adaptive assimilation of concepts and categories of multiple users through the social tagging system. The model has important implications on how collaborative systems could influence higher-level cognitive activities.

We describe the development of a computational cognitive model that explains navigation behavior on the World Wide Web. The model, called SNIF-ACT (Scent-based Navigation and Information Foraging in the ACT cognitive architecture), is motivated by Information Foraging Theory (IFT), which quantifies the perceived relevance of a Web link to a user’s goal by a spreading activation mechanism. The model assumes that users evaluate links on a Web page sequentially and decide to click on a link or to go back to the previous page by a Bayesian satisficing model (BSM) that adaptively evaluates and selects actions based on a combination of previous and current assessments of the relevance of link texts to information goals. SNIF-ACT 1.0 utilizes the measure of utility, called information Wai-Tat Fu is an applied cognitive scientist with interests in human–computer interaction, cognitive modeling, information seeking, interactive decision making, and cognitive skill acquisition; he is an Assistant Professor in the Human Factors Division and Beckman Institute of Advanced Science and Technology at the University

This article describes the development of a real-time model-based training system that provides adaptive “over-the-shoulder” (OTS) instructions to trainees as they learn to perform an Anti-Air Warfare Coordinator (AAWC) task. The long-term goal is to develop a system that will provide real-time instructional materials based on learners’ actions, so that eventually the initial set of instructions on a task can be strengthened, complemented, or overridden at different stages of training. The training system is based on the ACT-R architecture, which serves as the theoretical background for the cognitive model that monitors the learning process of the trainee. An experiment was designed to study the impact of OTS instructions on learning. Results showed that while OTS instructions facilitated short-term learning, (a) they took time away from the processing of current information, (b) their effects tended to decay rapidly in initial stages of training, and (c) their effects on training diminished when the OTS instructions were proceduralized in later stages of training. A cognitive model that learned from both the upfront and OTS instructions was created and provided good fits to the learning and performance data collected from human participants. Our results suggest that to fully capture the symbiotic performance between humans and intelligent training systems, it is important to closely monitor the learning process of the trainee so that instructional interventions can be delivered effectively at different stages of training. We proposed that such a flexible system can be developed based on an adaptive cognitive model that provides real-time predictions on learning and performance.

We have developed a methodology for studying and analyzing the psychology of users performing ecologically valid WWW tasks. A user trace is a record of all significant states and events in the user-WWW interaction based on eye tracking data, application-level logs, and think-aloud protocols. A user-tracing architecture has been implemented for developing simulation models of user-WWW interaction and for comparing a simulation model (SNIF-ACT) against user-trace data. The user tracing architecture compares each action of the SNIF-ACT simulation directly against observed user actions. The model and architecture have been used to successfully match detailed user trace data from four users working on two tasks each.

Memory can be internal or external - knowledge in-the-world or knowledge in-the-head. Making needed information available in an interface may seem the perfect alternative to relying on imperfect memory. However, the rational analysis framework (Anderson, 1990) suggests that least-effort tradeoffs may lead to imperfect performance even when perfect knowledge in-the-world is readily available. The implications of rational analysis for interactive behavior are investigated in two experiments. In experiment 1 we varied the perceptual-motor effort of accessing knowledge in-the-world as well as the cognitive effort of retrieving items from memory. In experiment 2 we replicated one of the experiment 1 conditions to collect eye movement data. The results suggest that milliseconds matter. Least-effort tradeoffs are adopted even when the absolute difference in effort between a perceptual-motor versus a memory strategy is small, and even when adopting a memory strategy results in a higher error rate and lower performance.