A natural conversational interface that allows longitudinal symptom tracking would be extremely valuable in health wellness applications. However, the task of designing emotionally-aware agents for behavior change is still poorly understood. In this paper, we present the design and evaluation of an emotion-aware chatbot that conducts experience sampling in an empathetic manner. We evaluate it through a human-subject experiment with N=39 participants over the course of a week. Our results show that extraverts preferred the emotion-aware chatbot significantly more than introverts. Also, participants reported a higher percentage of positive mood reports when interacting with the empathetic bot. Finally, we provide guidelines for the design of emotion-aware chatbots for potential use in mHealth contexts.
Despite the strengths of ESM, it also has limitations. This method puts heavy demand on research participants, which makes it a better fit for conscientious individuals rather than the general population @cite_13 . There have been multiple efforts in improving the average user's engagement and compliance. Some researchers have tried to reduce the burden on the user by incorporating ESM more seamlessly into their daily pipelines, for example, by placing it in the phone unlock screen @cite_1 . Other researchers have designed engaging games, making short questionnaires part of the game flow, and have validated ESM responses captured in the game in comparison to the traditional setting @cite_21 .
@cite_1 @cite_21 @cite_13
2024074057 2010376411 2134807463 2011974988
The purpose of this meta-analysis is to examine overall effect as well as the impact of selected instructional design principles in the context of virtual reality technology-based instruction (i.e. games, simulation, virtual worlds) in K-12 or higher education settings. A total of 13 studies (N?=?3081) in the category of games, 29 studies (N?=?2553) in the category of games, and 27 studies (N?=?2798) in the category of virtual worlds were meta-analyzed. The key inclusion criteria were that the study came from K-12 or higher education settings, used experimental or quasi-experimental research designs, and used a learning outcome measure to evaluate the effects of the virtual reality-based instruction.Results suggest games (FEM?=?0.77; REM?=?0.51), simulations (FEM?=?0.38; REM?=?0.41), and virtual worlds (FEM?=?0.36; REM?=?0.41) were effective in improving learning outcome gains. The homogeneity analysis of the effect sizes was statistically significant, indicating that the studies were different from each other. Therefore, we conducted moderator analysis using 13 variables used to code the studies. Key findings included that: games show higher learning gains than simulations and virtual worlds. For simulation studies, elaborate explanation type feedback is more suitable for declarative tasks whereas knowledge of correct response is more appropriate for procedural tasks. Students performance is enhanced when they conduct the game play individually than in a group. In addition, we found an inverse relationship between number of treatment sessions learning gains for games.With regards to the virtual world, we found that if students were repeatedly measured it deteriorates their learning outcome gains. We discuss results to highlight the importance of considering instructional design principles when designing virtual reality-based instruction. A comprehensive review of virtual reality-based instruction research.Analysis of the moderation effects of design features in a virtual environment.Using an advance statistical technique of meta-analysis to study the effects.Virtual reality environment is effective for teaching in K-12 and higher education.Results can be used by instructional designers to design the virtual environments. We study the performance and user experience of two popular mainstream mobile text entry methods: the Smart Touch Keyboard (STK) and the Smart Gesture Keyboard (SGK). Our first study is a lab-based ten-session text entry experiment. In our second study we use a new text entry evaluation methodology based on the experience sampling method (ESM). In the ESM study, participants installed an Android app on their own mobile phones that periodically sampled their text entry performance and user experience amid their everyday activities for four weeks. The studies show that text can be entered at an average speed of 28 to 39 WPM, depending on the method and the user's experience, with 1.0 to 3.6 character error rates remaining. Error rates of touchscreen input, particularly with SGK, are a major challenge; and reducing out-of-vocabulary errors is particularly important. Both SGK and STK have strengths, weaknesses, and different individual awareness and preferences. Two-thumb touch typing in a focused setting is particularly effective on STK, whereas one-handed SGK typing with the thumb is particularly effective in more mobile situations. When exposed to both, users tend to migrate from STK to SGK. We also conclude that studies in the lab and in the wild can both be informative to reveal different aspects of keyboard experience, but used in conjunction is more reliable in comprehensively assessing input technologies of current and future generations. The experience-sampling (ESM) technique is a method in which recording of feelings and activities is done on-line at the moment, either at randomly selected moments or at predetermined times. This method has the advantage of being able to not only assess people’s general feelings, but to link feelings with situations, times of day, and other circumstances. Thus, ESM provides a powerful way of moving beyond simple questions about who is “happy” and who is not, to more intricate questions about when and why people experience positive and negative feelings. Compared to retrospective reports of feelings, ESM is less influenced by memory biases. ESM also allows researchers to analyze the patterning and relationships of feelings as they unfold over time. Despite the strengths and promise of this method, there are also limitations. For example, the heavy demand placed on research participants means that the sample might be biased toward highly conscientious individuals, and repeatedly reporting one’s moods might itself influence feelings. How to analyze ESM data is discussed, including the issue of how to aggregate momentary feelings into global measures of the average subjective well-being of individuals. Estimating affective and cognitive states in conditions of rich human-computer interaction, such as in games, is a field of growing academic and commercial interest. Entertainment and serious games can benefit from recent advances in the field as, having access to predictors of the current state of the player (or learner) can provide useful information for feeding adaptation mechanisms that aim to maximize engagement or learning effects. In this paper, we introduce a large data corpus derived from 58 participants that play the popular Super Mario Bros platform game and attempt to create accurate models of player experience for this game genre. Within the view of the current research, features extracted both from player gameplay behavior and game levels, and player visual characteristics have been used as potential indicators of reported affect expressed as pairwise preferences between different game sessions. Using neuroevolutionary preference learning and automatic feature selection, highly accurate models of reported engagement, frustration, and challenge are constructed (model accuracies reach 91 , 92 , and 88 for engagement, frustration, and challenge, respectively). As a step further, the derived player experience models can be used to personalize the game level to desired levels of engagement, frustration, and challenge as game content is mapped to player experience through the behavioral and expressivity patterns of each player.