Interface design of educational games for middle school students

The purpose of this report is to identify interface design elements in educational games for a middle school audience. The four games in this report were selected based on their availability to the general public and their reputable developers. The purpose in undertaking this research is to determine interface and interaction conventions in educational games for 11-14 year olds in order to make recommendations to designers and developers. This report identifies the ways each game approaches important features such as art direction, providing feedback, portraying significant events, indicating clickable items, and event timing. It discusses the games’ techniques to guide gameplay. This report also addresses how interface design supports pedagogical goals.Curriculum and Instructio

Profiling Dating Apps Users: Sociodemographic and Personality Characteristics

The development of new technologies, the expansion of the Internet, and the emergence of dating apps (e.g., Tinder, Grindr) in recent years have changed the way to meet and approach potential romantic and/or sexual partners. The recent phenomenon has led to some gaps in the literature on individual differences (sociodemographic variables and personality traits) between users (previous and current users) and non-users of dating apps. Thus, the aim of this study was to analyze the relationship between using dating apps, sociodemographics (gender, age, sexual orientation, and relationship status), and bright and dark personality traits. Participants were 1705 university students (70% women, 30% men), aged between 18 and 26 (M = 20.60, SD = 2.09), who completed several online questionnaires. Through multinomial logistic regression analyses, it was found that men, older youth, and members of sexual minorities were more likely to be current and previous dating apps users. Being single and higher scores in open-mindedness were associated with higher probability to be current dating apps user. The dark personality showed no predictive ability. The discussion highlights the usefulness of knowing and considering the sociodemographic background and the characteristics of personality patterns in the design and implementation of preventive and promotion programs of healthy romantic and sexual relationships to improve people’s better health and well-being

Trends in Patient Generated Data – An Initial Review

In recent years, patient-centered care has gained significant momentum in healthcare and the patient is more involved as an active participant in data generation. In this state of the art review we identify trends in patient generated data (PGD) and areas in need of further research by reviewing papers published in the health tracks of five high-ranked IS conferences. Our results suggest that research is mostly empirically grounded and primarily focuses on sickness rather than wellness issues. There is an emphasis on chronic diseases and self-management, dealing with user motivation, and a focus mostly on mobile apps. Though technology plays an important part, there is scarce problematization of and theorization on PGD. Further studies are needed that investigate the effects of PGD on patients and healthcare providers, include a wider range of issues and incorporate wearable devices more comprehensively

Beyond Utility: An inductive investigation into non-utility factors influencing consumer adoption and use of ICT

This study explores the adoption and use of Information and Communications Technologies (ICTs) in a context marked by ubiquitous connectivity and intense social interaction. Research in the field has predominantly explored the topic within closed and private contexts, such as work and education environments. Resulting theories tend to lose predictive strength when transferred to open and social contexts. Specifically, theories often assume that behaviour is shaped exclusively by the utility derived from technological functions – an occurrence more common in closed and private settings. Other influencing factors, whilst acknowledged, tend to be sidelined or treated as exceptions. Further complexities arise as theorists misread and mistreat user perceptions and intentions. The study combines an inductive strategy with a Skinnerian radical behaviourist philosophical worldview. Individual accounts and group discussion about online social networking and smartphone ownership were captured in a natural social setting. A total of 35 technology users from Malta aged between 18 and 40 years participated in face-to-face interviews and focus group discussions. In contrast to other studies, verbal accounts and group interaction were treated and analysed as social behaviour and not as cognitive decision processes. Findings show that a more holistic understanding emerges if the social and internal dimensions are considered alongside environmental consequences. Results indicate that beyond utilitarian benefits, users also seek pleasure and social status whilst averting risk and minimising cost and disruption. The study shows that consumer ICTs are different from other technologies, such as cars and refrigerators, since these are tools specifically designed for application within verbal behaviour. ICTs can be applied as tools to communicate information, share past experiences, provide feedback to others, and confer social status on others. ICT applications elicit feedback from listeners and observers rather than cause measurable changes in the environment. The study builds on this insight by proposing a conceptual framework as an interpretative tool for practitioners and as a theoretic proposition for future inquiry

IDENAS: Internal Dependency Exploration for Neural Architecture Search

Machine learning is a powerful tool for extracting valuable information and making various predictions from diverse datasets. Traditional algorithms rely on well-defined input and output variables however, there are scenarios where the distinction between the input and output variables and the underlying, associated (input and output) layers of the model, are unknown. Neural Architecture Search (NAS) and Feature Selection have emerged as promising solutions in such scenarios. This research proposes IDENAS, an Internal Dependency-based Exploration for Neural Architecture Search, integrating NAS with feature selection. The methodology explores internal dependencies in the complete parameter space for classification involving 1D sensor and 2D image data as well. IDENAS employs a modified encoder-decoder model and the Sequential Forward Search (SFS) algorithm, combining input-output configuration search with embedded feature selection. Experimental results demonstrate IDENASs superior performance in comparison to other algorithms, showcasing its effectiveness in model development pipelines and automated machine learning. On average, IDENAS achieved significant modelling improvements, underscoring its significant contribution to advancing the state-of-the-art in neural architecture search and feature selection integration.Comment: 57 pages, 19 figures + appendix, the related software code can be found under the link: https://github.com/viharoszsolt/IDENA

Lesson Learned from Collecting Quantified Self Information via Mobile and Wearable Devices

The ubiquity and affordability of mobile and wearable devices has enabled us to continually and digitally record our daily life activities. Consequently, we are seeing the growth of data collection experiments in several scientific disciplines. Although these have yielded promising results, mobile and wearable data collection experiments are often restricted to a specific configuration that has been designed for a unique study goal. These approaches do not address all the real-world challenges of “continuous data collection” systems. As a result, there have been few discussions or reports about such issues that are faced when “implementing these platforms” in a practical situation. To address this, we have summarized our technical and user-centric findings from three lifelogging and Quantified Self data collection studies, which we have conducted in real-world settings, for both smartphones and smartwatches. In addition to (i) privacy and (ii) battery related issues; based on our findings we recommend further works to consider (iii) implementing multivariate reflection of the data; (iv) resolving the uncertainty and data loss; and (v) consider to minimize the manual intervention required by users. These findings have provided insights that can be used as a guideline for further Quantified Self or lifelogging studies

Logging Stress and Anxiety Using a Gamified Mobile-based EMA Application, and Emotion Recognition Using a Personalized Machine Learning Approach

According to American Psychological Association (APA) more than 9 in 10 (94 percent) adults believe that stress can contribute to the development of major health problems, such as heart disease, depression, and obesity. Due to the subjective nature of stress, and anxiety, it has been demanding to measure these psychological issues accurately by only relying on objective means. In recent years, researchers have increasingly utilized computer vision techniques and machine learning algorithms to develop scalable and accessible solutions for remote mental health monitoring via web and mobile applications. To further enhance accuracy in the field of digital health and precision diagnostics, there is a need for personalized machine-learning approaches that focus on recognizing mental states based on individual characteristics, rather than relying solely on general-purpose solutions. This thesis focuses on conducting experiments aimed at recognizing and assessing levels of stress and anxiety in participants. In the initial phase of the study, a mobile application with broad applicability (compatible with both Android and iPhone platforms) is introduced (we called it STAND). This application serves the purpose of Ecological Momentary Assessment (EMA). Participants receive daily notifications through this smartphone-based app, which redirects them to a screen consisting of three components. These components include a question that prompts participants to indicate their current levels of stress and anxiety, a rating scale ranging from 1 to 10 for quantifying their response, and the ability to capture a selfie. The responses to the stress and anxiety questions, along with the corresponding selfie photographs, are then analyzed on an individual basis. This analysis focuses on exploring the relationships between self-reported stress and anxiety levels and potential facial expressions indicative of stress and anxiety, eye features such as pupil size variation and eye closure, and specific action units (AUs) observed in the frames over time. In addition to its primary functions, the mobile app also gathers sensor data, including accelerometer and gyroscope readings, on a daily basis. This data holds potential for further analysis related to stress and anxiety. Furthermore, apart from capturing selfie photographs, participants have the option to upload video recordings of themselves while engaging in two neuropsychological games. These recorded videos are then subjected to analysis in order to extract pertinent features that can be utilized for binary classification of stress and anxiety (i.e., stress and anxiety recognition). The participants that will be selected for this phase are students aged between 18 and 38, who have received recent clinical diagnoses indicating specific stress and anxiety levels. In order to enhance user engagement in the intervention, gamified elements - an emerging trend to influence user behavior and lifestyle - has been utilized. Incorporating gamified elements into non-game contexts (e.g., health-related) has gained overwhelming popularity during the last few years which has made the interventions more delightful, engaging, and motivating. In the subsequent phase of this research, we conducted an AI experiment employing a personalized machine learning approach to perform emotion recognition on an established dataset called Emognition. This experiment served as a simulation of the future analysis that will be conducted as part of a more comprehensive study focusing on stress and anxiety recognition. The outcomes of the emotion recognition experiment in this study highlight the effectiveness of personalized machine learning techniques and bear significance for the development of future diagnostic endeavors. For training purposes, we selected three models, namely KNN, Random Forest, and MLP. The preliminary performance accuracy results for the experiment were 93%, 95%, and 87% respectively for these models

Psychophysiology in games

Psychophysiology is the study of the relationship between psychology and its physiological manifestations. That relationship is of particular importance for both game design and ultimately gameplaying. Players’ psychophysiology offers a gateway towards a better understanding of playing behavior and experience. That knowledge can, in turn, be beneficial for the player as it allows designers to make better games for them; either explicitly by altering the game during play or implicitly during the game design process. This chapter argues for the importance of physiology for the investigation of player affect in games, reviews the current state of the art in sensor technology and outlines the key phases for the application of psychophysiology in games.The work is supported, in part, by the EU-funded FP7 ICT iLearnRWproject (project no: 318803).peer-reviewe

An aesthetics of touch: investigating the language of design relating to form

How well can designers communicate qualities of touch? This paper presents evidence that they have some capability to do so, much of which appears to have been learned, but at present make limited use of such language. Interviews with graduate designer-makers suggest that they are aware of and value the importance of touch and materiality in their work, but lack a vocabulary to fully relate to their detailed explanations of other aspects such as their intent or selection of materials. We believe that more attention should be paid to the verbal dialogue that happens in the design process, particularly as other researchers show that even making-based learning also has a strong verbal element to it. However, verbal language alone does not appear to be adequate for a comprehensive language of touch. Graduate designers-makers’ descriptive practices combined non-verbal manipulation within verbal accounts. We thus argue that haptic vocabularies do not simply describe material qualities, but rather are situated competences that physically demonstrate the presence of haptic qualities. Such competencies are more important than groups of verbal vocabularies in isolation. Design support for developing and extending haptic competences must take this wide range of considerations into account to comprehensively improve designers’ capabilities