Open Process Models Optimizing Self Regulated Learning in the Classroom

Many of today’s computing advancements, including artificial intelligence (AI), machine learning and data science, rely on distributed and parallel computation. Thus, developing distributed and parallel systems skills is essential for computer science education and preparation for the workforce. Learning parallel programming, however, is difficult because students often find it challenging to think in parallel rather than sequentially (how they are taught in introductory courses). Game-based learning approaches can provide an engaging environment that fosters metacognition and self-regulation. This work builds on an NSF-funded game called Parallel (educational IIS 1523116), which engages students in solving puzzles that increase in difficulty, helping students to solve puzzles using abstract principles from parallel programming. Studies of the game, however, found that it alone cannot stimulate students to manage their learning through planning, reflection, re-assessment, and re-planning. This project will develop community-based tools based on learning sciences principles to scaffold students' self-regulated learning. Student learning processes will be visualized using interfaces that allow students to learn from each other and instructors to coach and scaffold the learning process. The project will focus on the research question of how to engage students in metacognitive processes of self-regulated learning toward better learning of parallel programming. The project will address this research question through design-based research. The project will develop a set of studies to understand how students currently learn and self-regulate their learning within parallel programming classes and then use the results of these studies to help develop an AI system with augmented community interaction mechanisms. This system will be composed of a process visualization system that extends the Parallel game to allow students to view and reflect on their process through an open visualization of their own and other students’ data revealing problem-solving strategies and decisions. The community interaction mechanisms will allow students to leave comments for one another and for teachers to scaffold the learning process through feedback on the student’s learning process. 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.