Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

The Cognitive-Affective-Social Theory of Learning in digital Environments (CASTLE)

For a long time, research on individuals learning in digital environments was primarily based on cognitive-oriented theories. This paper aims at providing evidence that social processes affect individual learning with digital materials. Based on these theories and empirical results, a social-processes-augmented theory is suggested: the Cognitive-Affective-Social Theory of Learning in digital Environments (CASTLE). This CASTLE postulates that social cues in digital materials activate social schemata in learners leading to enhanced (para-)social, motivational, emotional, and metacognitive processes. To substantiate this theory, socio-cognitive theories are used, which predict social influences on learning with digital materials. Besides, previous empirical findings are presented assuming that with a rising number of social cues in digital materials, the influence of social processes increases. Finally, consequences regarding the design of digital learning media are discussed

Developing an Algebra Textbook Based on Problem Solving to Improve Student' Learning Achievement

Students' mathematics learning achievement is influenced by many factors, one of which is the learning media used. The textbook is one alternative learning media. The textbook used previously was not focused on problem-solving, so that this development research aims to produce a textbook based on problem-solving in algebra that is valid, effective, and practical. This research uses Gall and Borg development research which is limited to seven steps namely research and information collecting, planning, developing a preliminary form of product, preliminary field testing, main product revisions, main field testing, and dissemination. This research was conducted at the Department of Mathematics Education, Universitas PGRI Madiun. Instruments and data collection used in this study were textbook validation sheets, learning outcomes tests, and student response questionnaire. The results of the research showed that the validation result of the textbook was included in the highly valid criteria (86,74 %), the test results met the criteria of classical completeness (82,75%), and the students' response to the textbook was very high (83,33%). Based on these results, it can be concluded that the algebra textbook based on problem-solving can be used in learning. The results of the application of an algebra textbook based on the problem-solving show an increase in student achievement with an N-Gain value of 0.32.

We might be wrong, but we think that hedging doesn't protect your reputation.

We gain much of our knowledge from other people. Since people are fallible - they lie, mislead, and are mistaken - it seems essential to monitor their claims and their reliability as sources of information. An intuitive way to do this is to draw on our expectations about claims and sources: to perform expectation-based updating (Hahn, Merdes, & von Sydow, 2018). But this updating can have damaging consequences, leading us into a kind of confirmation bias. An alternative is to keep track of outcomes and record whether a claim proves true or false: to perform outcome-based updating (Hahn et al., 2018). This form of updating does not have the negative repercussions on belief accuracy. But both forms of updating might undermine the trust and cooperation assumed to be necessary for successful communication. We explore a potential boundary condition on these types of updating. We investigate whether speakers can protect their reputation when they make claims with low prior probability, with and without knowledge of the final outcome. We explore suggestions from McCready (2015) that speakers can protect themselves by hedging with evidential language: in particular with weaker propositional attitudes ('I suspect that...') and so-called double hedges ('I might be wrong, but I think...'). We find that both forms of updating are robust to hedging with this evidential language, and find no clear evidence for a protective effect. We discuss extra ingredients that may allow successful hedging