Robert Christopherson

The effect of motivational learning companions on low achieving students and students with disabilities

We report the results of a randomized controlled evaluation of the effectiveness of pedagogical agents as providers of affective feedback. These digital learning companions were embedded in an intelligent tutoring system for mathematics, and were used by approximately one hundred students in two public high schools. Students in the control group did not receive the learning companions. Results indicate that low-achieving students—one third of whom have learning disabilities—had higher affective needs than their higher-achieving peers; they initially considered math problem-solving more frustrating, less exciting, and felt more anxious when solving math problems. However, after they interacted with affective pedagogical agents, low-achieving students improved their affective outcomes, e.g., reported reduced frustration and anxiety.

Animated agents and learning: Does the type of verbal feedback they provide matter?

The current study was conducted to investigate the effects of an animated agents presence and different types of feedback on learning, motivation and cognitive load in a multimedia-learning environment designed to teach science content. Participants were 135 college students randomly assigned to one of four experimental conditions formed by a 2 x 2 factorial design with agent presence as one factor (agent vs. no-agent) and type of verbal feedback it provided as the other factor (simple feedback vs. elaborate feedback). Results revealed that participants who learned with the animated agent that delivered elaborate feedback had significantly higher scores on a learning measure compared to participants who learned with an agent that provided simple feedback. The results are interpreted from both social agency and cognitive load theoretical perspectives.

Lost in the dark: emotion adaption

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 users 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.

How to Do Multimodal Detection of Affective States

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.

Affect Recognition in Learning Scenarios: Matching Facial- and BCI-Based Values

The ability of a learning system to infer a students affects has become highly relevant to be able to adjust its pedagogical strategies. Several methods have been used to infer affects. One of the most recognized for its reliability is face-based affect recognition. Another emerging one involves the use of brain-computer interfaces. In this paper we compare those strategies and explore if, to a great extent, it is possible to infer the values of one source from the other source.