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

Project: Research project

Project Details


There is an urgent need to train large numbers of computer scientists to meet the demands of the 21st century. While large numbers of students are entering the field by taking introductory computer science courses in formal settings (CS1), many struggle to learn programming, and the dropout rate for these courses is high. This problem is particularly pronounced for women and underrepresented minorities, who drop out at an even higher rate. CS1 students struggle with course content, but also struggle to plan their work effectively, seek help when needed, and reflect on their own understanding. These metacognitive skills are important for success. However, while many initiatives aim to help CS1 students learn content more effectively, few focus on helping students develop more effective learning strategies. As a result, there is a pressing need to more effectively support metacognition in CS1 to expand the number and diversity of trained computer scientists in the workforce. This proposal aims to improve student learning and retention in CS1 by providing personalized, context-aware scaffolds to help students develop effective learning strategies. A critical challenge in supporting student metacognition is that students struggle with a wide variety of strategies, and there are multiple underlying causes that lead to ineffective behaviors. While personalized mentoring could help students recognize and improve their strategies, this approach is not scalable to the large number of students typically enrolled in CS1 classes. Furthermore, ineffective behaviors arise across multiple contexts in the CS1 ecosystem, including lectures, office hours, and individual practice, making it difficult for teaching staff to identify struggles. The proposed research will enable Context-Aware Metacognition Practice (CAMP), namely the ability for students to recognize ineffective learning strategies and practice more effective strategies across CS1 contexts. This research will create cyberlearning technologies that (1) instrument classroom ecosystems to surface student learning strategies, (2) scaffold reflection on ineffective strategies and their underlying root causes, and (3) scaffold contextual practice of more effective strategies. Our research team from Northwestern Computer Science, the Segal Design Institute, and School of Education & Social Policy have expertise in cyberlearning, computer science education, self-directed learning, and advancing educational initiatives at scale.
Effective start/end date8/1/207/31/23


  • National Science Foundation (IIS 2016900)


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