CIDSE

The human-element is crucial for designing and implementing interactive intelligent systems, and therefore on instructional design. This tutorial provides a description and hands-on demonstration for detection of affective states and a description of devices, methodologies and tools necessary for automatic detection of affective states. Automatic detection of affective states requires that the computer sense information that is complex and diverse, it can range from brain-waves signals, and biofeedback readings to face-based and gesture emotion recognition to posture and pressure sensing. Obtaining, processing and understanding that information, to create systems that improve learning, requires the use of several sensing devices (and their perceiving algorithms) and the application of software tools.

The Affective Meta-Tutoring system is comprised of (1) a tutor that teaches system dynamics modeling, (2) a meta-tutor that teaches good strategies for learning how to model from the tutor, and (3) an affective learning companion that encourages students to use the learning strategy that the meta-tutor teaches. The affective learning companion's messages are selected by using physiological sensors and log data to determine the student's affective state. Evaluations compared the learning gains of three conditions: the tutor alone, the tutor plus meta-tutor and the tutor, meta-tutor and affective learning companion.

Having environments that are able to adjust accordingly with the user has been sought in the last years particularly in the area of Human Computer Interfaces. Environments able to recognize the user emotions and react in consequence have been of interest on the area of Affective Computing. This work presents a project -an adaptable 3D video game, Lost in the Dark: Emotion Adaption, which uses user's emotions as input to alter and adjust the gaming environment. To achieve this, an interface that is capable of reading brain waves, facial expressions, and head motion was used, an Emotiv® EPOC headset. For our purposes we read emotions such as meditation, excitement, and engagement into the game, altering the lighting, music, gates, colors, and other elements that would appeal to the user emotional state. With this, we achieve closing the loop of using the emotions as inputs, adjusting a system accordingly as a result, and elicit emotions.

Abstract: Meta-tutoring applies the basic policies of interactive tutoring to get students to adopt effective meta-cognitive strategies. Unfortunately, when the meta-tutor is removed, students often revert to using ineffective strategies. This paper is an early report on the progress of the Affective Meta-Tutoring (AMT) project, which will use an affective learning companion to motivate students to more permanently adopt effective meta-cognitive strategies. Keywords: Meta-cognition, self-regulated learning, intelligent tutoring systems, ...

Intelligent Tutoring Systems are capable of becoming an alternative to expert human tutors, able to provide direct customized instruction and feedback to students. Although Intelligent Tutoring Systems could differ widely in their attached knowledge bases and user interfaces (including interaction mechanisms), their behaviors are quite similar; thus, it must be possible to establish a common software model for them. A common software model is a step forward to move these systems from proof-of-concepts and academic research tools to widely available tools in schools and homes. The work reported here addresses: (1) the use of Design Patterns to create an object-oriented software model for Intelligent Tutoring Systems; (2) the application of the model into a two-year development project; (3) the qualities achieved and trade-offs made. Besides that, this paper describes our experience using patterns, and the impact in facts such as creating a common language among stakeholders, supporting an incremental development and adjustment to a highly shifting developing team.

The computer's ability to recognize human emotional states given physiological signals is gaining in popularity to create empathetic systems such as learning environments, health care systems and videogames. Despite that, there are few frameworks, libraries, architectures, or software tools, which allow systems developers to easily integrate emotion recognition into their software projects. The work reported here offers a first step to fill this gap in the lack of frameworks and models, addressing: (a) the modeling of an agent-driven component-based architecture for multimodal emotion recognition, called ABE, and (b) the use of ABE to implement a multimodal emotion recognition framework to support third-party systems becoming empathetic systems.

Intelligent Tutoring Systems (ITSs) constitute an alternative to expert human tutors, providing direct customized instruction and feedback to students. ITSs could positively impact education if adopted on a large scale, but doing that requires tools to enable their mass production. This circumstance is the key motivation for this work. We present a component-based approach for a system architecture for ITSs equipped with meta-tutoring and affective capabilities. We elicited the requirements that those systems might address and created a system architecture that models their structure and behavior to drive development efforts. Our experience applying the architecture in the incremental implementation of a four-year project is discussed.

User modeling in AIED has been extended in the past decades to include affective and motivational aspects of learner's interaction in intelligent tutoring systems. In order to study those factors, various detectors have been created that classify episodes in log data as gaming, high/low effort on task, robust learning, etc. In this article, we present our method for creating a detector of shallow modeling practices within a meta-tutor instructional system. The detector was defined using HCI (human-computer interaction) task modeling as well as a coding scheme defined by human coders from past users' screen recordings of software use. The detector produced classifications of student behavior that were highly similar to classifications produced by human coders with a kappa of .925.

Research in affective computing and educational technology has shown the potential of affective interventions to increase student's self-concept and motivation while learning. Our project aims to investigate whether the use of affective interventions in a meta-cognitive tutor can help students achieve deeper modeling of dynamic systems by being persistent in their use of metacognitive strategies during and after tutoring. This article is an experience report on how we designed and implemented the affective intervention. (The meta-tutor is described in a separate paper.) We briefly describe the theories of affect underlying the design and how the agent's affective behavior is defined and implemented. Finally, the evaluation of a detector-driven categorization of student behavior, that guides the agent's affective interventions, against a categorization performed by human coders, is presented.

There is growing interest about personalizing a computer's responses according to a user's affective states (emotions). This is particularly useful for learning, health care, and entertainment systems. However, systems able to recognize, understand, and respond to human affect are still designed and developed from scratch and the experience from their implementation is rarely documented, resulting in forcing developers to "re-invent the wheel" each time. This paper presents an approach to record the design knowledge related to affective systems in the form of patterns, aiming to develop a pattern language for them.

The objective is to classify a group of subjects playing a video game as experts and novices using electroencephalogram (EEG) signals as inputs. Analytical methods applied to multi-channel EEG recording are described. A fast Fourier transform (FFT) is used to calculate the power spectral density for a number of bandwidths (delta, theta, alpha and beta) and ratios (e.g., theta/beta). A regularized logistic regression learning algorithm (L 2 penalty) was applied to the extracted features. We successfully classified 80% of the instances using a 10 fold cross-validation.

There is growing interest in how to leverage information about users’ emotions as a mean of personalizing the response of computer systems. This is particularly useful for computer-aided learning, health, and entertainment systems. Such systems are still designed and developed from scratch and the experience from their implementation is not documented, resulting in forcing the development teams to ‘re-invent the wheel’. Therefore, there are few architectures, frameworks, libraries, or software tools that allow developers to easily integrate emotion recognition into their software projects. This paper presents an approach of recording the design experience in the form of patterns for emotional-aware systems and aims to develop a pattern language for those systems.

Abstract. Intelligent Tutoring Systems are capable of becoming an alternative to expert human tutors, able to provide direct customized instruction and feedback to students. Although Intelligent Tutoring Systems could differ widely in their attached knowledge bases and user interfaces (including interaction mechanisms), their behaviors are quite similar; thus, it must be possible to establish a common software model for them. A common software model is a step forward to move these systems from proof-of-concepts and academic research tools to widely available tools in schools and homes. The work reported here addresses: (1) the use of Design Patterns to create an object-oriented software model for Intelligent Tutoring Systems; (2) the application of the model into a two-year development project; (3) the qualities achieved and trade-offs made. Besides that, this paper describes our experience using patterns, and the impact in facts such as creating a common language among stakeholders...

: We introduce connectivity editing operations to control irregular vertices in quadrilateral meshes. This can lead to improved results in the design of a glass structure: (a) top: the original mesh with irregular vertices as colored dots, (a) bottom: a stripe pattern applied to the mesh, (b) a rendering of the design as glass construction. In (c) and (d) we show the edited mesh. The glass panels on the roof are generated from the edges in the meshes.

: In our framework, the user can edit irregular vertices (with more or fewer than four neighbors) and irregular faces (non-quads) of a quad-dominant mesh. Each type of irregularity has different advantages and disadvantages in design and construction. Irregular vertices are necessary to maintain sharp features (corners and edges), but they create higher angle deviations in mesh lines in smooth regions (left). Irregular faces lead to smoother mesh lines, but they cannot maintain sharp features (right). The ability to model with a mixture of irregular vertices and faces gives more flexibility to the user, e.g. creating a design with sharp features and smooth mesh lines (middle). We render each model in a style that highlights the sharp features.

We present a framework for exploring topologically unique quadrangulations of an input shape. First, the input shape is segmented into surface patches. Second, different topologies are enumerated and explored in each patch. This is realized by an efficient subdivision-based quadrangulation algorithm that can exhaustively enumerate all mesh topologies within a patch. To help users navigate the potentially huge collection of variations, we propose tools to preview and arrange the results. Furthermore, the requirement that all patches need to be jointly quadrangulatable is formulated as a linear integer program. Finally, we apply the framework to shape-space exploration, remeshing, and design to underline the importance of topology exploration.

Internet and social media devices created a new public space for online debate on political and social topics. A debate is defined as a formal discussion on a set of related topics in a public meeting, in which opposing perspectives and arguments are put forward. In this paper, we develop automated perspective discovery techniques which would contribute to the understanding of features (i.e. social, political, cultural, religious beliefs, goals, and practices) shared by each side of the debate. Secondly, we show that, compared to a semi-automated process, our perspective discovery algorithms not only identify larger number of relevant features, but they also yield a higher accuracy scaling of moderate to extreme organizations on both sides of a debate.