E-Learning systems can support real-time monitoring of learners? learning desires and effects, thus offering opportunities for enhanced personalized learning. Recognition of the determinants of dyslexic users? motivation to use e-learning systems is important to help developers improve the design of e-learning systems and educators direct their efforts to relevant factors to enhance dyslexic students? motivation. Existing research has rarely attempted to model dyslexic users? motivation in e-learning context from a comprehensive perspective. This paper has conceived a hybrid approach, namely combining the strengths of qualitative and quantitative analysis methods, to motivation modeling. It examines a variety of factors which affect dyslexic students? motivation to engage in e-learning systems from psychological, behavioral and technical perspectives, and establishes their interrelationships. Specifically, the study collects data from a multi-item Likert-style questionnaire to measure relevant factors for conceptual motivation modelling. It then applies the Structural Equation Modeling approach to determine the quantitative mapping between dyslexic students? continued use intention and motivational factors, followed by discussions about theoretical findings and design instructions according to our motivation model. Our research has led to a novel motivation model with new constructs of Learning Experience, Reading Experience, Perceived Control and Perceived Privacy. Initial results have indicated direct effects of Attitudes Toward School, Visual Attractiveness, Reading Experience and Utilization on continued use intention.
Adapting user interface designs for specific tasks performed by different users is a challenging yet important problem. Automatically adapting visualization designs to users and contexts (e.g., tasks, display devices, environments, etc.) can theoretically improve human-computer interaction to acquire insights from complex datasets. However, effectiveness of any specific visualization is moderated by individual differences in knowledge, skills and abilities for different contexts. A modeling framework called Personalized Recommender System for Information visualization Methods via Extended matrix completion (PRIME) is proposed for recommending the optimal visualization designs for individual users in different contexts. PRIME quantitatively models covariates (e.g., psychological and behavioral measurements) to predict recommendation scores (e.g., perceived complexity, mental workload, etc.) for users to adapt the visualization specific to the context. An evaluation study was conducted and showed that the PRIME can achieve satisfactory recommendation accuracy for adapting visualization, even when there are limited historical data. PRIME can make accurate recommendation even for new users or new tasks based on historical wearable sensor signals and recommendation scores. This capability contributes to designing new generation of visualization systems that adapt to users? status in real time. PRIME can support researchers in reducing the sample size requirements to quantify individual differences, and practitioners in adapting visualizations according to user states and contexts in real time.
Identifying people in historical photographs is important for preserving material culture, correcting the historical record, and creating economic value, but it is also a complex and challenging task. In this paper, we focus on identifying portraits of soldiers who participated in the American Civil War (1861--65), the first widely-photographed conflict. Many thousands of these portraits survive, but only 10-20% are identified. We created Photo Sleuth, a web-based platform that combines crowdsourced human expertise and automated face recognition to support Civil War portrait identification. Our mixed-methods evaluations of Photo Sleuth one month and 11 months after its public launch showed that it helped users successfully identify unknown portraits and provided a sustainable model for volunteer contribution. We also discuss implications for crowd-AI interaction and person identification pipelines.
Personality detection is an important task in psychology, as different personality traits are linked to different behaviours and real-life outcomes. Traditionally it involves filling out lengthy questionnaires which is time consuming, and may also be unreliable if respondents do not fully understand the questions or are not willing to honestly answer them. In this paper, we propose a framework for objective personality detection that leverages humans' physiological responses to external stimuli. We exemplify and evaluate the framework in a case study, where we expose subjects to affective image and video stimuli, and capture their physiological responses using non-invasive commercial-grade eye-tracking and skin conductivity sensors. These responses are then processed and used to build a machine learning classifier capable of accurately predicting a wide range of personality traits. We investigate and discuss the performance of various machine learning methods, the most and least accurately predicted traits, and also assess the importance of the different stimuli, features, and physiological signals. Our work demonstrates that personality traits can be accurately detected, suggesting the applicability of the proposed framework for robust personality detection and use by psychology practitioners and researchers, as well as designers of personalised interactive systems.
We present an empirical study with humans not trained in AI, to investigate the impact of two types of explanations on non-experts' understanding of reinforcement learning (RL) agents. The study was a 124 participant, four-treatment experiment to compare participants' mental models of an RL agent, in a context of a simple Real-Time Strategy (RTS) game. The four treatments isolated the two types of explanations vs. neither vs. both together. The two types of explanations were: (1) saliency maps (an "Input Intelligibility Type" that explains the AI's focus of attention), and (2) a recent explanation type, reward-decomposition bars (an "Output Intelligibility Type" that explains the AI's predictions of future types of rewards). Our results show that the combination of both saliency and decomposed reward bars was needed to achieve a statistically significant difference in participants' mental model description scores over the no-explanation treatment. However, this combination of explanations was far from a panacea: They exacted disproportionately high cognitive loads from the participants. Further, in some situations, participants who saw both explanations predicted the agent's next action worse than all other treatments' participants.
Our research aims to develop interactive, social agents that can coach people to learn new tasks, skills, and habits. In this paper, we focus on coaching sedentary, overweight individuals (i.e., "trainees") to exercise regularly. We employ adaptive goal setting in which the intelligent health coach generates, tracks, and revises personalized exercise goals for a trainee. The goals become incrementally more difficult as the trainee progresses through the training program. Our approach is model-based - the coach maintains a parameterized model of the trainee?s aerobic capability that drives its expectation of the trainee?s performance. The model is continually revised based on trainee-coach interactions. The coach is embodied in a smartphone application which serves as a medium for coach-trainee interaction. We adopt a task-centric evaluation approach for studying the utility of the proposed algorithm in promoting regular aerobic exercise. We show that our approach can adapt the trainee program not only to several trainees with different capabilities, but also to how a trainee?s capability improves as they begin to exercise more. Experts rate the goals selected by the coach better than other plausible goals, demonstrating that our approach is consistent with clinical recommendations. Further, in a 6-week observational study with sedentary participants, we show that the proposed approach. helps increase exercise volume performed each week.
Recommender systems are ubiquitous, and shape the way users access information and make decisions. As these systems become more complex, there is a growing need for transparency and interpretability. In this paper, we study the problem of generating and visualizing personalized explanations for recommender systems which incorporate signals from many different data sources. We use a flexible, extendable probabilistic programming approach, and show how we can generate real-time personalized recommendations. We then turn these personalized recommendations into explanations. We perform an extensive user study to evaluate the benefits of explanations for hybrid recommender systems. We conduct a crowd-sourced user study where our system generates personalized recommendations and explanations for real users of the last.fm music platform. First, we evaluate the performance of the recommendations in terms of perceived accuracy and novelty. Next, we experiment with 1) different explanation styles (e.g., user-based, item-based), 2) manipulating the number of explanation styles presented, and 3) manipulating the presentation format (e.g., textual vs. visual). We also apply a mixed-model statistical analysis to consider user personality traits as a control variable and demonstrate the usefulness of our approach in creating personalized hybrid explanations with different style, number, and format. Finally, we perform a post analysis which shows different preferences for explanation styles between experienced and novice last.fm users.
In this paper, we propose novel techniques to predict a user?s movie genre preference and rating behavior from her psycholinguistic attributes obtained from the social media interactions. The motivation of this work comes from various psychological studies that demonstrate that psychological attributes such as personality and values can influence one?s decision or choice in real life. In this work, we integrate user interactions in Twitter and IMDb to derive interesting relations between human psychological attributes and their movie preferences. In particular, we first predict a user?s movie genre preferences from the personality and value scores of the user derived from her tweets. Second, we also develop models to predict user movie rating behavior from her tweets in Twitter and movie genre and storyline preferences from IMDb. We further strengthen the movie rating model by incorporating the user reviews. In the above models, we investigate the role of personality and values independently and combinedly while predicting movie genre preferences and movie rating behaviors. We find that our combined models significantly improve the accuracy than that of a single model that is built by using personality or values independently. We also compare our technique with the traditional movie genre and rating prediction techniques. The experimental results show that our models are effective in recommending movies to users.
This paper presents an online algorithms for personal preference modeling to estimate context-dependent user selection by using combination of a data-driven approach and sequential user feedback. The proposed approach is based on an algorithm of online prediction with experts in which our online learner uses a set of policies generated by supervised learning with collected other users' data and adapts to a target user by learning and tracking the optimal policy that best predicts the target user's selection. In each time step, the learner compares a user selection and each prediction from all policies. We also propose an alternative algorithm for a more challenging setting in which our learner is allowed to access limited numbers of policies in each time step (limited prediction setting). We applied the proposed approach to image filter selection as a concrete application. A series of evaluations of our algorithm revealed that 1) our online learning approach achieved better prediction accuracy and less regret than traditional supervised learning or bandit approaches, 2) prediction accuracy increased along with the number of policies, and 3) the proposed algorithm for the limited prediction setting outperformed the state-of-the-art algorithm when the learner obtained predictions from fewer than half the total number of policies. Results of our pilot user study also suggest the potential effectiveness of presenting prediction results from our algorithm that makes users' selection processes more efficient.
The primary well-controlled study of 900 participants found that personal presentation choices in professional emails (non-content changes such as Profile Avatar & Signature) impact the recipient?s perception of the sender?s personality, and the quality of the email itself. By understanding the role these choices play, employees can gain better control over how they influence the recipient of their messages. Results further indicated that while some variations can positively impact the recipient?s view of the sender, these same variations often also have negative side-efects. This implies that many seemingly innocuous presentation decisions should be made in the context of who is receiving the email, and if these effects negatively impact the content of the message. For example, while statements in a Signature about the email having being written on a phone are included to preemptively apologize for typing mistakes, this causes the sender to appear less agreeable, less conscientious, and less open, and the email itself appears less well written and more poorly formatted even though the email itself did not change in the study.