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Automated Detection of Engagement Using Video-Based Estimation of Facial Expressions and Heart Rate
TLDR
We explored how computer vision techniques can be used to detect engagement while students completed a structured writing activity (draft-feedback-review) similar to activities encountered in educational settings. Expand
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Automatic Detection of Learning-Centered Affective States in the Wild
TLDR
In this paper, computer vision and machine learning techniques were used to detect students' affect as they used an educational game designed to teach fundamental principles of Newtonian physics. Expand
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Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game
TLDR
We investigate the relationships among incoming knowledge, persistence, affective states, in-game progress, and consequently learning outcomes for students using the game Physics Playground. Expand
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Improving automated source code summarization via an eye-tracking study of programmers
TLDR
We present an eye-tracking study of 10 professional Java programmers in which the programmers read Java methods and wrote English summaries of those methods. Expand
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What Emotions Do Novices Experience during Their First Computer Programming Learning Session?
TLDR
We conducted a study to track the emotions, their behavioral correlates, and relationship with performance when novice programmers learned the basics of computer programming in the Python language. Expand
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Using Video to Automatically Detect Learner Affect in Computer-Enabled Classrooms
TLDR
We use computer vision and machine-learning techniques to detect students’ affect from facial expressions (primary channel) and gross body movements (secondary channel) during interactions with an educational physics game. Expand
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The Affective Experience of Novice Computer Programmers
TLDR
An analysis of affect-learning relationships after partialling out control variables (e.g., scholastic aptitude, hint usage) indicated that there were reciprocal transitions between engagement and confusion, confusion and frustration, and one way transitions between frustration and boredom and engagement. Expand
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Expert Feature-Engineering vs. Deep Neural Networks: Which Is Better for Sensor-Free Affect Detection?
TLDR
This paper compares several deep neural network approaches with a traditional feature engineering approach in the context of affect and behavior modeling. Expand
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Attending to Attention: Detecting and Combating Mind Wandering during Computerized Reading
Mind wandering (MW) is a ubiquitous phenomenon that has a negative influence on performance and productivity in many contexts. We propose that intelligent interfaces should have some mechanism toExpand
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Sequential Patterns of Affective States of Novice Programmers
TLDR
We explore the sequences of affective states that students experience during their first encounter with computer programming. Expand
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