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This book offers readers an overview of the state-of-the-art and emerging themes in affective computing, including a comprehensive review of the existing approaches to affective computing systems and social signal processing. It provides in-depth case studies of applied affective computing in various domains, such as social robotics and mental well-being. It also addresses ethical concerns related to affective computing and how to prevent misuse of the technology in research and applications. Further, this book identifies future directions for the field and summarizes a set of guidelines for developing next-generation affective computing systems that are effective, safe, and human-centered.

From a theoretical viewpoint, educational interfaces that facilitate communicative actions involving representations central to a domain can maximize students' effort associated with constructing new schemas. In addition, interfaces that minimize working memory demands due to the interface per se, for example by mimicking existing non-digital work practice, can preserve students' attentional focus on their learning task. In this research, we asked the question: What type of interface input capabilities provide best support for science problem solving in both low- and high-performing students? High school students' ability to solve a diverse range of biology problems was compared over longitudinal sessions while they used: (1) hardcopy paper and pencil (2) a digital paper and pen interface (3) pen tablet interface, and (4) graphical tablet interface. Post-test evaluations revealed that time to solve problems, meta-cognitive control, solution correctness, and memory all were significantly enhanced when using the digital pen and paper interface, compared with tablet interfaces. The tangible pen and paper interface also was the only alternative that significantly facilitated skill acquisition in low-performing students. Paradoxically, all students nonetheless believed that the tablet interfaces provided best support for their performance, revealing a lack of self-awareness about how to use computational tools to best advantage. Implications are discussed for how pen interfaces can be optimized for future educational purposes, and for establishing technology fluency curricula to improve students' awareness of the impact of digital tools on their performance.

Current graphical keyboard and mouse interfaces are better suited for handling mechanical tasks, like email and text editing, than they are at supporting focused problem solving or complex learning tasks. One reason is that graphical interfaces limit users’ ability to fluidly express content involving different representational systems (e.g., symbols, diagrams) as they think through steps during complex problem solutions. We asked: Can interfaces be designed that actively stimulate students’ ability to “think on paper,” including providing better support for both ideation and convergent problem solving? In this talk, we will summarize new research on the affordances of different types of interface (e.g., pen-based, keyboard-based), and how these basic computer input capabilities function to substantially facilitate or impede people’s ideational fluency. We also will show data on the relation between interface support for communicative fluency (i.e., both linguistic and non-linguistic forms) and ideational fluency. In addition, we’ll discuss the relation between interface support for active marking (i.e., both formal structures like diagrams, and informal ones such as “thinking marks”) and successful problem solving. Finally, we’ll present new data on interfaces that improve support for learning and performance in lower-performing populations, and we will discuss how these new directions in interface media could play a role in improving their education and minimizing the persistent achievement gap between low- versus high-performing groups 

Optimism is growing that the near future will witness rapid growth in human-computer interaction using voice. System prototypes have recently been built that demonstrate speaker-independent real-time speech recognition, and understanding of naturally spoken utterances with vocabularies of 1000 to 2000 words, and larger. Already, computer manufacturers are building speech recognition subsystems into their new product lines. However, before this technology can be broadly useful, a substantial knowledge base is needed about human spoken language and performance during computer-based spoken interaction. This paper reviews application areas in which spoken interaction can play a significant role, assesses potential benefits of spoken interaction with machines, and compares voice with other modalities of human-computer interaction. It also discusses information that will be needed to build a firm empirical foundation for the design of future spoken and multimodal interfaces. Finally, it argues for a more systematic and scientific approach to investigating spoken input and performance with future language technology.

This chapter reviews the main types of multimodal interfaces, their advantages and cognitive science underpinnings, primary features and architectural characteristics, and general research in the field of multimodal interaction and interface design. Multimodal systems have developed rapidly during the past decade, with steady progress toward building more general and robust systems, as well as more transparent human interfaces than ever before. Major developments have occurred in the hardware and software needed to support key component technologies incorporated within multimodal systems, as well as in techniques for integrating parallel input streams. Current natural language-processing algorithms typically rely heavily on the specification of determiners in definite and indefinite references in order to represent and resolve noun phrase reference. Over the past decade, numerous advantages of multimodal interface design have been documented. Unlike a traditional keyboard-and-mouse interface or a unimodal recognition-based interface, multimodal interfaces permit flexible use of input modes.

Conversational search is based on a user-system cooperation with the objective to solve an information-seeking task. In this report, we discuss the implication of such cooperation with the learning perspective from both user and system side. We also focus on the stimulation of learning through a key component of conversational search, namely the multimodality of communication way, and discuss the implication in terms of information retrieval. We end with a research road map describing promising research directions and perspectives.