This report aims to 'crack the code' by deciphering the factors that hinder and facilitate girls' and women's participation, achievement and continuation in science, technology, engineering and mathematics (STEM) education and, in particular, what the education sector can do to promote girls' and women's interest in and engagement with STEM education and ultimately STEM careers.

This monograph integrates theoretical perspectives on affect and learning with recent research in affective computing with an emphasis on building new learning technologies. The "new perspectives" come from the intersection of several research themes: -Basic research on emotion, cognition, and motivation applied to learning environments -Pedagogical and motivational strategies that are sensitive to affective and cognitive processes -Multimodal Human Computer Interfaces, with a focus on affect recognition and synthesis -Recent advances in affect-sensitive Intelligent Tutoring Systems -Novel methodologies to investigate affect and learning -Neuroscience research on emotions and learning

Education for Sustainable Development (ESD) is globally acknowledged as a powerful driver of change, empowering learners to make decisions and take actions needed to build a just and economically viable societ y respect ful of both the environment and cultural diversit y.

Traditionally, cognition and emotion are seen as separate domains that are independent at best and in competition at worst. The French scientist and philosopher Blaise Pascal (1623-1662) famously said “Le coeur a ses raisons que la raison ne connaît point” (The heart has its reasons that reason does not know). Over the last century, however, psychologists and neuroscientists have increasingly appreciated their very strong reciprocal connections and interactions. Initially this was demonstrated in cognitive functions such as attention, learning and memory, and decision making. For instance, an emotional stimulus captures attention (e.g., Anderson & Phelps, 2001). Likewise, emotional stimuli are better learned and remembered than neutral ones (e.g., McGaugh, 1990) and they can provide strong incentives to bias decision making (Bechara et al., 1997). In more recent years, cognitive control has also been found to be intimately intertwined with emotion. This is consistent with an approach that considers cognitive control as an adaptive learning process (Braver & Cohen, 1999), reinforcement learning in particular (Holroyd & Coles, 2002; Verguts & Notebaert, 2009). From this perspective, cognitive control is not a cool encapsulated executive function, but instead involves rapidly calculating the value of situational, contextual, and action cues (Rushworth & Behrens, 2008) for the purpose of adapting the cognitive system toward future optimal performance. A wide array of research has shed light on cognitive control and its interactions with affect or motivation. Behaviorally, important phenomena include how people respond to difficult stimuli (e.g., incongruent stimuli, task switches), negative feedback, or errors and how this influences subsequent task processing. Neurally, an important target structure has been the anterior cingulate cortex (ACC) and its connections to traditional “emotional” (e.g., amygdala) and “cognitive” areas (e.g., (pre)motor cortex, dorsolateral prefrontal cortex). ACC seems to play a predominant role in integrating distant effects from remote cognitive and emotion systems in order to guide and optimize behavior. The current special issue focuses on the bi-directional link between emotion and cognitive control. We invite studies that investigate the influence from emotion on cognitive control, or vice versa, the influence of cognitive control on emotion. Contributions can be of different types: We welcome empirical contributions (behavioral or neuroscientific) but also computational modeling, theory, or review papers. By bringing together researchers from the traditionally separated domains, we hope to further stimulate the crosstalk between emotion and cognitive control, and thus to deepen our understanding of both.

"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 our 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. Thus, 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. Moreover, 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 Special Issue on Socio-Cognitive and Affective Computing aimed at integrating these various albeit complementary fields. Proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems were welcome. Designing this kind of system requires combining knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing. Papers with a special focus on multidisciplinary approaches and multimodality were especially welcome"--Page 1.

Metacognition, Cognition, and Human Performance, Volume 2: Instructional Practices is a collection of papers that deals with applied settings that develop and test instructional programs in the field of education. The book discusses some insights in understanding the processes involved in writing and reading. The text defines metacognition — as a mental function and the directing of this function — and reading, as well as the structure of narratives. One paper proposes a model for cognitive monitoring and early reading by developing for children three knowledge domains: function of print, form of print, and conventions of print or metacognitive constructs. Other papers analyze metacognition, instruction, the role of questioning activities, as well as the connection between metacognition and learning disabilities. One author evaluates a different perspective whether attention-related difficulties are a normal development in a young child or a disability in the older child. This author also explains meta-attention pertaining to task solving, selective attention to other stimuli, and visual search of the surrounding or for a target object. One research shows that methodologies designed to induce underachieving children to regulate their own academic behavior can improve their performance. The text can prove useful to child psychologists, behavioral scientists, and students and professors in child education.