Joseph B. Wiggins

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Learning involves a rich array of cognitive and affective states. Recognizing and understanding these cognitive and affective dimensions of learning is key to designing informed interventions. Prior research has highlighted the importance of facial expressions in learning-centered affective states, but tracking facial expression poses significant(More)
Detecting learning-centered affective states is difficult, yet crucial for adapting most effectively to users. Within tutoring in particular, the combined context of student task actions and tutorial dialogue shape the student's affective experience. As we move toward detecting affect, we may also supplement the task and dialogue streams with rich sensor(More)
A variety of studies have established that users with different personality profiles exhibit different patterns of behavior when interacting with a system. Although patterns of behavior have been successfully used to predict cognitive and affective outcomes of an interaction, little work has been done to identify the variations in these patterns based on(More)
Introductory computer science courses cultivate the next generation of computer scientists. The impressions students take away from these courses are crucial, setting the tone for the rest of the students' computer science education. It is known that students struggle with many concepts central to computer science, struggles that could be alleviated in part(More)
Affective and cognitive processes form a rich substrate on which learning plays out. Affective states often influence progress on learning tasks, resulting in positive or negative cycles of affect that impact learning outcomes. Developing a detailed account of the occurrence and timing of cognitive-affective states during learning can inform the design of(More)
Learners experience a wide array of cognitive and affective states during tutoring. Detecting and responding to these states is a core problem of adaptive learning environments that aim to foster motivation and increase learning. Recognizing learner affect through nonverbal behavior is particularly challenging, as students display affect across numerous(More)
Understanding how students solve computational problems is central to computer science education research. This goal is facilitated by recent advances in the availability and analysis of detailed multimodal data collected during student learning. Drawing on research into student problem-solving processes and findings on human posture and gesture, this(More)
In recent years, significant advances have been made in intelligent tutoring systems, and these advances hold great promise for adaptively supporting computer science (CS) learning. In particular, tutorial dialogue systems that engage students in natural language dialogue can provide rich, adaptive interactions. One promising approach for increasing the(More)
Tutorial dialogue is a highly effective way to support student learning. It is widely recognized that tutor dialogue moves can significantly influence learning outcomes, but the ways in which tutor moves, student affective response, and outcomes are related remains an open question. This paper presents an analysis of student affective response, as evidenced(More)
Emotion, or affect, plays a central role in learning. In particular, promoting positive emotions throughout the learning process is important for students' motivation to pursue computer science and for retaining computer science students. Positive emotions, such as engagement or enjoyment, may be fostered by timely individualized help. Especially promising(More)