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.
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.
Eating activity monitoring through wearable sensors can potentially enable interventions based on eating speed to mitigate the risks of critical healthcare problems such as obesity or diabetes. Eating actions are poly-componential gestures composed of sequential arrangements of three distinct components interspersed with gestures that may be unrelated to eating. This makes it extremely challenging to accurately identify eating actions. The primary reason for the lack of acceptance of state-of-art eating action monitoring techniques include: i) the need to install wearable sensors that are cumbersome to wear or limit mobility of the user, ii) the need for manual input from the user, and iii) poor accuracy if adequate manual input is not available. In this work, we propose a novel methodology, IDEA that performs accurate eating action identification in eating episodes with an average F1-score of 0.92. IDEA uses only a single wrist-worn sensor and provides feedback on eating speed every 2 minutes without obtaining any manual input from the user. %It can also be used to automatically annotate other poly-componential gestures.
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.
This is the introduction to the special issue of ACM Transactions on Interactive Intelligent Systems containing highlights of the ACM Intelligent User Interface (IUI) 2018 conference.
A proposal for a unified theory of learned trust implemented in a cognitive architecture is presented. A published computational cognitive model of learned trust is critically reviewed. A revised model is proposed to overcome the limitations of the published model and expand its scope of applicability. The revised model integrates several seemingly unrelated categories of findings from the literature on interpersonal and human-machine interactions and makes unintuitive predictions for future studies. The implications of the model for the advancement of the theory on trust are discussed.