Involving Users to Improve the Collaborative Logical Framework

In order to support collaboration in web-based learning, there is a need for an intelligent support that facilitates its management during the design, development, and analysis of the collaborative learning experience and supports both students and instructors. At aDeNu research group we have proposed the Collaborative Logical Framework (CLF) to create effective scenarios that support learning through interaction, exploration, discussion, and collaborative knowledge construction. This approach draws on artificial intelligence techniques to support and foster an effective involvement of students to collaborate. At the same time, the instructors’ workload is reduced as some of their tasks—especially those related to the monitoring of the students behavior—are automated. After introducing the CLF approach, in this paper, we present two formative evaluations with users carried out to improve the design of this collaborative tool and thus enrich the personalized support provided. In the first one, we analyze, following the layered evaluation approach, the results of an observational study with 56 participants. In the second one, we tested the infrastructure to gather emotional data when carrying out another observational study with 17 participants

Annotating Affect in the Field: A Case Study on the Usability of a Minimalist Smartwatch User Interface for Affect Annotation

Successful empathetic interaction requires an accurate understanding of the interaction partner\u27s affect dynamics. Self-reported annotations provide a way to better understand affect and empathy in real-life; however, the necessary user interactions for collecting such data must be designed to be as unobtrusive as possible. To address this challenge, we explore the potential of a smartwatch annotation application for affect that aims to minimize user interaction effort while maximizing usability. In a field study conducted as part of a student career fair (N=9), we evaluated the feasibility and usability of our app. Participants reported high usability scores and our data collection successfully captured self-reported affect labels at a high temporal resolution. Our work contributes to the challenge of providing minimal obtrusive applications for the collection of self-reported labels of affective states

Comparison of engagement and emotional responses of older and younger adults interacting with 3D cultural heritage artefacts on personal devices

The availability of advanced software and less expensive hardware allows museums to preserve and share artefacts digitally. As a result, museums are frequently making their collections accessible online as interactive, 3D models. This could lead to the unique situation of viewing the digital artefact before the physical artefact. Experiencing artefacts digitally outside of the museum on personal devices may affect the user's ability to emotionally connect to the artefacts. This study examines how two target populations of young adults (18–21 years) and the elderly (65 years and older) responded to seeing cultural heritage artefacts in three different modalities: augmented reality on a tablet, 3D models on a laptop, and then physical artefacts. Specifically, the time spent, enjoyment, and emotional responses were analysed. Results revealed that regardless of age, the digital modalities were enjoyable and encouraged emotional responses. Seeing the physical artefacts after the digital ones did not lessen their enjoyment or emotions felt. These findings aim to provide an insight into the effectiveness of 3D artefacts viewed on personal devices and artefacts shown outside of the museum for encouraging emotional responses from older and younger people

Biometric storyboards: a games user research approach for improving qualitative evaluations of player experience

Developing video games is an iterative and demanding process. It is difficult to achieve the goal of most video games — to be enjoyable, engaging and to create revenue for game developers — because of many hard-to-evaluate factors, such as the different ways players can interact with the game. Understanding how players behave during gameplay is of vital importance to developers and can be uncovered in user tests as part of game development. This can help developers to identify and resolve any potential problem areas before release, leading to a better player experience and possibly higher game review scores and sales. However, traditional user testing methods were developed for function and efficiency oriented applications. Hence, many traditional user testing methods cannot be applied in the same way for video game evaluation. This thesis presents an investigation into the contributions of physiological measurements in user testing within games user research (GUR). GUR specifically studies the interaction between a game and users (players) with the aim to provide feedback for developers to help them to optimise the game design of their title. An evaluation technique called Biometric Storyboards is developed, which visualises the relationships between game events, player feedback and changes in a player’s physiological state. Biometric Storyboards contributes to the field of human-computer interaction and GUR in three important areas: (1) visualising mixedmeasures of player experience, (2) deconstructing game design by analysing game events and pace, (3) incremental improvement of classic user research techniques (such as interviews and physiological measurements). These contributions are described in practical case studies, interviews with game developers and laboratory experiments. The results show this evaluation approach can enable games user researchers to increase the plausibility and persuasiveness of their reports and facilitate developers to better deliver their design goals. Biometric Storyboards is not aimed at replacing existing methods, but to extend them with mixed methods visualisations, to provide powerful tools for games user researchers and developers to better understand and communicate player needs, interactions and experiences. The contributions of this thesis are directly applicable for user researchers and game developers, as well as for researchers in user experience evaluation in entertainment systems

Return of the man-machine interface: violent interactions

This paper presents the design and evaluation of “the man-machine interface” a punchable interface designed to criticise and react against the values inherent in modern systems that tacitly favour one type of user (linguistically and technically gifted) and alienate another (physically gifted). We report a user study, where participants used the device to express their opinions before engaging in a group discussion about the implications of strength-based interactions. We draw connections between our own work and that of evolutionary biologists whose recent findings indicate the shape of the human hand is likely to have been partly evolved for the purpose of punching, and conclude by examining violent force as an appropriate means for expressing thoughts and feelings

Essays on Health Information Technology: Insights from Analyses of Big Datasets

The current dissertation provides an examination of health information technology (HIT) by analyzing big datasets. It contains two separate essays focused on: (1) the evolving intellectual structure of the healthcare informatics (HI) and healthcare IT (HIT) scholarly communities, and (2) the impact of social support exchange embedded in social interactions on health promotion outcomes associated with online health community use. Overall, this dissertation extends current theories by applying a unique combination of methods (natural language processing, machine learning, social network analysis, and structural equation modeling etc.) to the analyses of primary datasets. The goal of the first study is to obtain a full understanding of the underlying dynamics of the intellectual structures of HI and its sub-discipline HIT. Using multiple statistical methods including citation and co-citation analysis, social network analysis (SNA), and latent semantic analysis (LSA), this essay shows how HIT research has emerged in IS journals and distinguished itself from the larger HI context. The research themes, intellectual leadership, cohesion of these themes and networks of researchers, and journal presence revealed in our longitudinal intellectual structure analyses foretell how, in particular, these HI and HIT fields have evolved to date and also how they could evolve in the future. Our findings identify which research streams are central (versus peripheral) and which are cohesive (as opposed to disparate). Suggestions for vibrant areas of future research emerge from our analysis. The second part of the dissertation focuses on comprehensively understanding the effect of social support exchange in online health communities on individual members’ health promotion outcomes. This study examines the effectiveness of online consumer-to-consumer social support exchange on health promotion outcomes via analyses of big health data. Based on previous research, we propose a conceptual framework which integrates social capital theory and social support theory in the context of online health communities and test it through a quantitative field study and multiple analyses of a big online health community dataset. Specifically, natural language processing and machine learning techniques are utilized to automate content analysis of digital trace data. This research not only extends current theories of social support exchange in online health communities, but also sheds light on the design and management of such communities

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisakis model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: Linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers error rates and then to evaluate the information expressed by the classifiers confusion matrices. © Springer Science+Business Media, LLC 2011