NSF Cyberlearning: Context-Aware Metacognitive Practice: Instrumenting Classroom Ecosystems to Help Introductory Computer Science Students Develop Effective Learning Strategies

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Technology is increasingly important across all areas of our society, and as a result, there is a pressing need to train more computer scientists. While many students are taking introductory computer science courses (CS1) in college, they often struggle to learn programming. CS1 concepts are challenging, but students also struggle to plan their work effectively, seek help when needed, and reflect on their understanding. Such skills, which are metacognitive, are important for successful learning. While many initiatives aim to help students learn content more effectively, few focus on helping students develop these learning strategies. To address this gap, this project will develop new technologies that provide personalized, contextually relevant supports and practice opportunities to help students develop metacognitive skills in large CS1 classes.Supporting metacognition is challenging because students struggle with different planning, reflection, and help-seeking skills, and their struggles arise across a diverse set of contexts. While personalized mentoring is effective, this approach is not scalable to the large number of students enrolled in CS1 classes. This project aims to overcome these challenges through “Context-Aware Metacognition Practice (CAMP),” a theoretical model and platform that supports metacognition by: (1) instrumenting classroom ecosystems to surface student learning strategies, (2) scaffolding reflection on ineffective strategies and their underlying root causes, and (3) scaffolding contextual practice of more effective strategies. This research will study the effectiveness of the CAMP model using a design-based research approach to generate empirically validated principles that advance our understanding of metacognitive scaffolding.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.