Person: Reinecke, Katharina
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Reinecke
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Katharina
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Reinecke, Katharina
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Publication Doodle Around the World: Online Scheduling Behavior Reflects Cultural Differences in Time Perception and Group Decision-Making(ACM Press, 2013) Reinecke, Katharina; Nguyen, Minh Khoa; Bernstein, Abraham; Näf, Michael; Gajos, KrzysztofEvent scheduling is a group decision-making process in which social dynamics influence people's choices and the overall outcome. As a result, scheduling is not simply a matter of finding a mutually agreeable time, but a process that is shaped by social norms and values, which can highly vary between countries. To investigate the influence of national culture on people's scheduling behavior we analyzed more than 1.5 million Doodle date/time polls from 211 countries. We found strong correlations between characteristics of national culture and several behavioral phenomena, such as that poll participants from collectivist countries respond earlier, agree to fewer options but find more consensus than predominantly individualist societies. Our study provides empirical evidence of behavioral differences in group decision-making and time perception with implications for cross-cultural collaborative work.Publication SPRWeb: preserving subjective responses to website colour schemes through automatic recolouring(ACM Press, 2013) Flatla, David R.; Reinecke, Katharina; Gutwin, Carl; Gajos, KrzysztofColours are an important part of user experiences on the Web. Colour schemes influence the aesthetics, first impressions and long-term engagement with websites. However, five percent of people perceive a subset of all colours because they have colour vision deficiency (CVD), resulting in an unequal and less-rich user experience on the Web. Traditionally, people with CVD have been supported by recolouring tools that improve colour differentiability, but do not consider the subjective properties of colour schemes while recolouring. To address this, we developed SPRWeb, a tool that recolours websites to preserve subjective responses and improve colour differentiability - thus enabling users with CVD to have similar online experiences. To develop SPRWeb, we extended existing models of non-CVD subjective responses to CVD, then used this extended model to steer the recolouring process. In a lab study, we found that SPRWeb did significantly better than a standard recolouring tool at preserving the temperature and naturalness of websites, while achieving similar weight and differentiability preservation. We also found that recolouring did not preserve activity, and hypothesize that visual complexity influences activity more than colour. SPRWeb is the first tool to automatically preserve the subjective and perceptual properties of website colour schemes thereby equalizing the colour-based web experience for people with CVD.Publication Crowdsourcing performance evaluations of user interfaces(ACM Press, 2013) Komarov, Steven; Reinecke, Katharina; Gajos, KrzysztofOnline labor markets, such as Amazon's Mechanical Turk (MTurk), provide an attractive platform for conducting human subjects experiments because the relative ease of recruitment, low cost, and a diverse pool of potential participants enable larger-scale experimentation and faster experimental revision cycle compared to lab-based settings. However, because the experimenter gives up the direct control over the participants' environments and behavior, concerns about the quality of the data collected in online settings are pervasive. In this paper, we investigate the feasibility of conducting online performance evaluations of user interfaces with anonymous, unsupervised, paid participants recruited via MTurk. We implemented three performance experiments to re-evaluate three previously well-studied user interface designs. We conducted each experiment both in lab and online with participants recruited via MTurk. The analysis of our results did not yield any evidence of significant or substantial differences in the data collected in the two settings: All statistically significant differences detected in lab were also present on MTurk and the effect sizes were similar. In addition, there were no significant differences between the two settings in the raw task completion times, error rates, consistency, or the rates of utilization of the novel interaction mechanisms introduced in the experiments. These results suggest that MTurk may be a productive setting for conducting performance evaluations of user interfaces providing a complementary approach to existing methodologies.Publication Predicting users' first impressions of website aesthetics with a quantification of perceived visual complexity and colorfulness(ACM Press, 2013) Reinecke, Katharina; Yeh, Tom; Miratrix, Luke; Mardiko, Rahmatri; Zhao, Yuechen; Liu, Jenny; Gajos, KrzysztofUsers make lasting judgments about a website's appeal within a split second of seeing it for the first time. This first impression is influential enough to later affect their opinions of a site's usability and trustworthiness. In this paper, we demonstrate a means to predict the initial impression of aesthetics based on perceptual models of a website's colorfulness and visual complexity. In an online study, we collected ratings of colorfulness, visual complexity, and visual appeal of a set of 450 websites from 548 volunteers. Based on these data, we developed computational models that accurately measure the perceived visual complexity and colorfulness of website screenshots. In combination with demographic variables such as a user's education level and age, these models explain approximately half of the variance in the ratings of aesthetic appeal given after viewing a website for 500ms only.Publication Accurate Measurements of Pointing Performance from In Situ Observations(Association for Computing Machinery, 2012) Gajos, Krzysztof; Reinecke, Katharina; Herrmann, CharlesWe present a method for obtaining lab-quality measurements of pointing performance from unobtrusive observations of natural in situ interactions. Specifically, we have developed a set of user-independent classifiers for discriminating between deliberate, targeted mouse pointer movements and those movements that were affected by any extraneous factors. To develop and validate these classifiers, we developed logging software to unobtrusively record pointer trajectories as participants naturally interacted with their computers over the course of several weeks. Each participant also performed a set of pointing tasks in a formal study set-up. For each movement, we computed a set of measures capturing nuances of the trajectory and the speed, acceleration, and jerk profiles. Treating the observations from the formal study as positive examples of deliberate, targeted movements and the in situ observations as unlabeled data with an unknown mix of deliberate and distracted interactions, we used a recent advance in machine learning to develop the classifiers. Our results show that, on four distinct metrics, the data collected in-situ and filtered with our classifiers closely matches the results obtained from the formal experiment.