Real-time Metacognition Feedback for Introductory Programming Using Machine Learning.

This is a Work in Progress Research to Practice Category paper. Research has shown that novice programmers struggle with learning introductory concepts and find it difficult to monitor their own progress. Teachers often have hundreds of students and multiple sections of programming courses to teach, making it infeasible to provide the amount of independent feedback each student may need to flourish. With limited instructor feedback, students who can self-monitor and self-assess their programming metacognition have a higher chance of developing a process for solving programming challenges. In this paper, we expand on the literate programming paradigm by using natural language processing and machine learning methods to automatically analyze and classify student programming metacognition levels through their source code comments. Our intent is to ultimately integrate our classification models into an interactive developer environment to provide real-time feedback to students about their metacognition while learning to program.