The problem of which cues, internal or external, permit a person to label and identify his own emotional state has been with us since the days that James (1890) first tendered his doctrine that \"the bodily changes follow directly the perception of the exciting fact, and that our feeling of the same changes as they occur is the emotion\" (p. 449). Since we are aware of a variety of feeling and emotion states, it should follow from James' proposition that the various emotions will be accompanied by a variety of differentiable bodily states. Following James' pronouncement, a formidable number of studies were undertaken in search of the physiological differentiators of the emotions. The results, in these early days, were almost uniformly negative. All of the emotional states experi-

"Cognitive, social, and physiological determinants of emotional state": Erratum

Hawaii International Conference on System Sciences

Argumentation is an important skill to learn. It is valuable not only in many professional contexts, such as the law, science, politics, and business, but also in everyday life. However, not many people are good arguers. In response to this, researchers and practitioners over the past 15–20 years have developed software tools both to support and teach argumentation. Some of these tools are used in individual fashion, to present students with the “rules” of argumentation in a particular domain and give them an opportunity to practice, while other tools are used in collaborative fashion, to facilitate communication and argumentation between multiple, and perhaps distant, participants. In this paper, we review the extensive literature on argumentation systems, both individual and collaborative, and both supportive and educational, with an eye toward particular aspects of the past work. More specifically, we review the types of argument representations that have been used, the various types of interaction design and ontologies that have been employed, and the system architecture issues that have been addressed. In addition, we discuss intelligent and automated features that have been imbued in past systems, such as automatically analyzing the quality of arguments and providing intelligent feedback to support and/or tutor argumentation. We also discuss a variety of empirical studies that have been done with argumentation systems, including, among other aspects, studies that have evaluated the effect of argument diagrams (e.g., textual versus graphical), different representations, and adaptive feedback on learning argumentation. Finally, we conclude by summarizing the “lessons learned” from this large and impressive body of work, particularly focusing on lessons for the CSCL research community and its ongoing efforts to develop computer-mediated collaborative argumentation systems.

/pdf/computer-supported-argumentation-a-review-of-the-state-of-2sceqh29qr.pdf

Computer-Supported Argumentation: A Review of the State of the Art

Stress detection in daily life scenarios using smart phones and wearable sensors: A survey.

Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

Human Emotion Recognition: Review of Sensors and Methods.

This paper reports an experiment for stress recognition in human-computer interaction. Thirty-one healthy participants performed five stressful HCI tasks and their skin conductance signals were monitored. The selected tasks were most frequently listed as stressful by 15 typical computer users who were involved in pre-experiment interviews asking them to identify stressful cases of computer interaction. The collected skin conductance signals were analyzed using seven popular machine learning classifiers. The best stress recognition accuracy was achieved by the cubic support vector machine classifier both per task (on average 90.8 %) and for all tasks (Mean = 98.8 %, SD = 0.6 %). This very high accuracy demonstrates the potentials of using physiological signals for stress recognition in the context of typical HCI tasks. In addition, the results allow us to move on a first integration of the specific stress recognition mechanism in PhysiOBS, a previously-proposed software tool that supports researchers and practitioners in user emotional experience evaluation.

/pdf/recognizing-emotions-in-human-computer-interaction-studying-46audxkgd5.pdf

Recognizing Emotions in Human Computer Interaction: Studying Stress Using Skin Conductance

CoPe_it! is an innovative Web-based tool that complies with collaborative practices to provide members of communities with the appropriate means to manage individual and collective knowledge during a sense-making and/or decision-making session. In this article, we demonstrate its applicability in tackling cognitively-complex collaboration settings, which are characterized by big volumes of interrelated data obtained from diverse sources and knowledge expressed by diverse participants. We focus on issues related to the representation of such settings and propose an approach to make it easier for participants to follow the evolution of collaboration, comprehend it in its entirety, and meaningfully aggregate data to resolve the issue under consideration.

Tackling Cognitively-Complex Collaboration with CoPe_it!

Measuring users' emotional reaction to interactive multimedia and hypermedia is important. One particularly popular self-reported method for emotion assessment is the Valence-Arousal (VA) Scale: a 9ź×ź9 affective grid. This paper aims to identify specific stress region(s) in the VA space by combining self-reported ratings (pairs of VA) and physiological signals (skin conductance). To this end, 31 healthy volunteers participated in an experiment by performing five stressful interaction tasks while their skin conductance was monitored. The selected interaction tasks were most frequently listed as stressful by a separate group of 15 interviewees. After each task, participants expressed their perceived emotional experience using the VA rating space. Our findings show which regions in the VA rating space may reliably indicate self-reported stress that is in alignment with one's measured skin conductance while using interactive applications. One additional important contribution of this work is the proposed approach for the empirical identification of affect regions in the VA space based on physiological signals.

Stress in interactive applications: analysis of the valence-arousal space based on physiological signals and self-reported data

Names play a key role in distributed hypertext systems, for two main reasons: Firstly, because accessing and managing system services require finding and locating the relevant components. Secondly, because managing structures between hypertext resources, such as nodes, anchors and links, requires that these resources are named and addressed. We argue that naming services are endemic to hypertext systems and therefore, form a core part of any hypertext system’s infrastructure. In particular, the current move towards interoperable component-based Open Hypermedia Systems (CB-OHS) demonstrates the need for naming components.

Naming as a fundamental concept of open hypermedia systems

The unprecedented growth of Web 2.0 has affected learning and has made the growth of learning networks possible. Learning networks are shaped by communities to help their members acquire knowledge in specific areas and are the most notable feature of Learning 2.0, the new learning era that focuses on individual learning needs. The evolution of learning forces traditional Learning Management Systems LMS to incorporate more Web 2.0 features and slowly transform to Personal Learning Environments PLEs. A Personal Learning Environment is a loosely structured collection of tools with strong social networking characteristics, which gives users the ability to create, maintain, and redistribute their own learning content. This paper is a field study of the most well-known and established LMSs and their support for specific features within several categories of tools of Web 2.0. The incorporation of Web 2.0 features within those LMSs differentiates them regarding their ability and potential to be used as PLEs.

Nikos Karousos

Papers

Recognizing Emotions in Human Computer Interaction: Studying Stress Using Skin Conductance

Tackling Cognitively-Complex Collaboration with CoPe_it!

Stress in interactive applications: analysis of the valence-arousal space based on physiological signals and self-reported data

Naming as a fundamental concept of open hypermedia systems

Learning Management Systems and Learning 2.0