Playing to Program: Towards an Intelligent Programming Tutor for RUR-PLE

Author/Creator ORCID

Date

2011-08-09

Department

Program

Citation of Original Publication

Marie desJardins, Amy Ciavolino, Robert Deloatch, and Eliana Feasley, Playing to Program: Towards an Intelligent Programming Tutor for RUR-PLE, Proceedings of the Second Symposium on Educational Advances in Artificial Intelligence, 2011, https://www.google.com/url?q=https://aaai.org/ocs/index.php/EAAI/EAAI11/paper/download/3497/4010&sa=U&ved=0ahUKEwjK377Z7LDeAhWkTN8KHXXgDKoQFggEMAA&client=internal-uds-cse&cx=016314354884912110518:gwmynp16xuu&usg=AOvVaw1oM4dZxvzFkVpco6n_3x-9

Rights

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Abstract

Intelligent tutoring systems (ITSs) provide students with a one-on-one tutor, allowing them to work at their own pace, and helping them to focus on their weaker areas. The RUR Python Learning Environment (RUR-PLE), a game-like virtual environment to help students learn to program, provides an interface for students to write their own Python code and visualize the code execution (Roberge 2005). RUR-PLE provides a fixed sequence of learning lessons for students to explore. We are extending RUR-PLE to develop the Playing to Program (PtP) ITS, which consists of three components: (1) a Bayesian student model that tracks student competence, (2) a diagnosis module that provides tailored feedback to students, and (3) a problem selection module that guides the student’s learning process. In this paper, we summarize RUR-PLE and the PtP design, and describe an ongoing user study to evaluate the predictive accuracy of our student modeling approach