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We present a lab-in-the-field experiment to demonstrate how individual behavior in the lab predicts their ability to identify phishing attempts. Using the business and finance staff members from a large public university in the U.S., we find that participants who are intolerant of risk, more curious, and less trusting commit significantly more errors when evaluating interfaces. We also replicate prior results on demographic correlates of phishing vulnerability, including age, gender, and education level. Our results suggest that behavioral characteristics such as intolerance of risk, curiosity, and trust can be used to predict individual ability to identify phishing interfaces.

Generating multiple-choice questions is known to improve students' critical thinking and deep learning. Visualizing relationships between concepts enhances meaningful learning, students' ability to relate new concepts to previously learned concepts. We designed and deployed a collaborative learning process through which students generate multiple-choice questions and represent the prerequisite knowledge structure between questions as visual links in a shared map, using a variation of Concept Maps that we call "QMap." We conducted a four-month study with 19 undergraduate students. Students sustained voluntary contributions, creating 992 good questions, and drawing 1, 255 meaningful links between the questions. Through analyzing self-reports, observations, and usage data, we report on the technical and social design features that led students to sustain their motivation.

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