Computer Science

The rapid development of virtual reality technology has increased its availability and, consequently, increased the number of its possible applications. The interest in the new medium has grown due to the entertainment industry (games, VR experiences and movies). The number of freely available training and therapeutic applications is also increasing. Contrary to popular opinion, new technologies are also adopted by older adults. Creating virtual environments tailored to the needs and capabilities of older adults requires intense research on the behaviour of these participants in the most common situations, towards commonly used elements of the virtual environment, in typical sceneries. Comfortable immersion in a virtual environment is key to achieving the impression of presence. Presence is, in turn, necessary to obtain appropriate training, persuasive and therapeutic effects. A virtual agent (a humanoid representation of an algorithm or artificial intelligence) is often an element of the virtual environment interface. Maintaining an appropriate distance to the agent is, therefore, a key parameter for the creator of the VR experience. Older (65+) participants maintain greater distance towards an agent (a young white male) than younger ones (25-35). It may be caused by differences in the level of arousal, but also cultural norms. As a consequence, VR developers are advised to use algorithms that maintain the agent at the appropriate distance, depending on the user's age.

Interpretability methods aim to help users build trust in and understand the capabilities of machine learning models. However, existing approaches often rely on abstract, complex visualizations that poorly map to the task at hand or require non-trivial ML expertise to interpret. Here, we present two interface modules to facilitate a more intuitive assessment of model reliability. To help users better characterize and reason about a model's uncertainty, we visualize raw and aggregate information about a given input's nearest neighbors in the training dataset. Using an interactive editor, users can manipulate this input in semantically-meaningful ways, determine the effect on the output, and compare against their prior expectations. We evaluate our interface using an electrocardiogram beat classification case study. Compared to a baseline feature importance interface, we find that 9 physicians are better able to align the model's uncertainty with clinically relevant factors and build intuition about its capabilities and limitations.

Misinformation about critical issues such as climate change and vaccine safety is oftentimes amplified on online social and search platforms. The crowdsourcing of content credibility assessment by laypeople has been proposed as one strategy to combat misinformation by attempting to replicate the assessments of experts at scale. In this work, we investigate news credibility assessments by crowds versus experts to understand when and how ratings between them differ. We gather a dataset of over 4,000 credibility assessments taken from 2 crowd groups---journalism students and Upwork workers---as well as 2 expert groups---journalists and scientists---on a varied set of 50 news articles related to climate science, a topic with widespread disconnect between public opinion and expert consensus. Examining the ratings, we find differences in performance due to the makeup of the crowd, such as rater demographics and political leaning, as well as the scope of the tasks that the crowd is assigned to rate, such as the genre of the article and partisanship of the publication. Finally, we find differences between expert assessments due to differing expert criteria that journalism versus science experts use---differences that may contribute to crowd discrepancies, but that also suggest a way to reduce the gap by designing crowd tasks tailored to specific expert criteria. From these findings, we outline future research directions to better design crowd processes that are tailored to specific crowds and types of content.

Background. The current cognitive state, such as cognitive effort and depletion, incidental affect or stress may impact the strength of a chosen password unconsciously. Aim. We investigate the effect of incidental fear and stress on the measured strength of a chosen password. Method. We conducted two experiments with within-subject designs measuring the Zxcvbn \textsf{log10} number of guesses as strength of chosen passwords as dependent variable. In both experiments, participants were signed up to a site holding their personal data and, for the second run a day later, asked under a security incident pretext to change their password. (a) Fear. N F =34 participants were exposed to standardized fear and happiness stimulus videos in random order. (b) \textbf{Stress.} N S =50 participants were either exposed to a battery of standard stress tasks or left in a control condition in random order. The Zxcvbn password strength was compared across conditions. Results. We did not observe a statistically significant difference in mean Zxcvbn password strengths on fear (Hedges' g av =−0.11 , 95\% CI [−0.45,0.23] ) or stress (and control group, Hedges' g av =0.01 , 95\% CI [−0.31,0.33] ). However, we found a statistically significant cross-over interaction of stress and TLX mental demand. Conclusions. While having observed negligible main effect size estimates for incidental fear and stress, we offer evidence towards the interaction between stress and cognitive effort that vouches for further investigation.

Millions of people have enrolled and enrol (especially in the Covid-19 pandemic world) in MOOCs. However, the retention rate of learners is notoriously low. The majority of the research work on this issue focuses on predicting the dropout rate, but very few use explainable learning patterns as part of this analysis. However, visual representation of learning patterns could provide deeper insights into learners' behaviour across different courses, whilst numerical analyses can -- and arguably, should -- be used to confirm the latter. Thus, this paper proposes and compares different granularity visualisations for learning patterns (based on clickstream data) for both course completers and non-completers. In the large-scale MOOCs we analysed, across various domains, our fine-grained, fish-eye visualisation approach showed that non-completers are more likely to jump forward in their learning sessions, often on a 'catch-up' path, whilst completers exhibit linear behaviour. For coarser, bird-eye granularity visualisation, we observed learners' transition between types of learning activity, obtaining typed transition graphs. The results, backed up by statistical significance analysis and machine learning, provide insights for course instructors to maintain engagement of learners by adapting the course design to not just 'dry' predicted values, but explainable, visually viable paths extracted.

Telerehabilitation systems for stroke survivors have been predominantly designed to measure and quantify movement in order to guide and encourage rehabilitation regular exercises at home. We set out to study what aspect of the movement data was essential, to better inform sensor design. We investigated face-to-face stroke rehabilitation sessions through a series of interviews and observations involving 16 stroke rehabilitation specialists including physiatrists, physical therapists, and occupational therapists. We found that specialists are not solely interested in movement data, and that experiential information about stroke survivors' lived experience plays an essential role in specialists interpreting movement data and creating a rehabilitation plan. We argue for a reconceptualization in stroke telerehabilitation that is more inclusive of non-movement data, and present design implications to better account for experiential information in telerehabilitation systems.

Effective adoption of online platforms for teaching, learning, and skill development is essential to both academic institutions and workplaces. Adoption of online learning has been abruptly accelerated by COVID19 pandemic, drawing attention to research on pedagogy and practice for effective online instruction. Online learning requires a multitude of skills and resources spanning from learning management platforms to interactive assessment tools, combined with multimedia content, presenting challenges to instructors and organizations. This study focuses on ways that learning sciences and visual learning analytics can be used to design, and to improve, online workforce training in advanced manufacturing. Scholars and industry experts, educational researchers, and specialists in data analysis and visualization collaborated to study the performance of a cohort of 900 professionals enrolled in an online training course focused on additive manufacturing. The course was offered through MITxPro, MIT Open Learning is a professional learning organization which hosts in a dedicated instance of the edX platform. This study combines learning objective analysis and visual learning analytics to examine the relationships among learning trajectories, engagement, and performance. The results demonstrate how visual learning analytics was used for targeted course modification, and interpretation of learner engagement and performance, such as by more direct mapping of assessments to learning objectives, and to expected and actual time needed to complete each segment of the course. The study also emphasizes broader strategies for course designers and instructors to align course assignments, learning objectives, and assessment measures with learner needs and interests, and argues for a synchronized data infrastructure to facilitate effective just in time learning and continuous improvement of online courses.

Mobile games can contribute to learning at greater success. In this paper, we have developed and evaluated a novel educational game, named FoodCalorie, to learn the food calorie intake standards. Our game is aimed to learn the calorie values of various traditional Bangladeshi foods and the calorie intake standard that varies with age and gender. Our study confirms the finding of existing studies that game-based learning can enhance the learning experience.

Social Network Sites (SNSs) like Facebook or Instagram are spaces where people expose their lives to wide and diverse audiences. This practice can lead to unwanted incidents such as reputation damage, job loss or harassment when pieces of private information reach unintended recipients. As a consequence, users often regret to have posted private information in these platforms and proceed to delete such content after having a negative experience. Risk awareness is a strategy that can be used to persuade users towards safer privacy decisions. However, many risk awareness technologies for SNSs assume that information about risks is retrieved and measured by an expert in the field. Consequently, risk estimation is an activity that is often passed over despite its importance. In this work we introduce an approach that employs deleted posts as risk information vehicles to measure the frequency and consequence level of self-disclosure patterns in SNSs. In this method, consequence is reported by the users through an ordinal scale and used later on to compute a risk criticality index. We thereupon show how this index can serve in the design of adaptive privacy nudges for SNSs.

We contribute a method to automate parameter configurations for chart layouts by learning from human preferences. Existing charting tools usually determine the layout parameters using predefined heuristics, producing sub-optimal layouts. People can repeatedly adjust multiple parameters (e.g., chart size, gap) to achieve visually appealing layouts. However, this trial-and-error process is unsystematic and time-consuming, without a guarantee of improvement. To address this issue, we develop Layout Quality Quantifier (LQ2), a machine learning model that learns to score chart layouts from pairwise crowdsourcing data. Combined with optimization techniques, LQ2 recommends layout parameters that improve the charts' layout quality. We apply LQ2 on bar charts and conduct user studies to evaluate its effectiveness by examining the quality of layouts it produces. Results show that LQ2 can generate more visually appealing layouts than both laypeople and baselines. This work demonstrates the feasibility and usages of quantifying human preferences and aesthetics for chart layouts.