Teaching programming (with or without AI)
In 2024, our seminars focus on the newest research into how to best teach programming at school — with and without the use of AI tools. Watch recordings, read our summary blogs, and download speakers' slides.
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Using generative AI to create personalised Parson's Problems and explanations (9 January 2024)
Speakers: Barbara Ericson and Xinying Hou (University of Michigan)
Parson's Problems can be used to scaffold students who struggle while writing code from scratch. In a Parson's Problem, learners are given mixed-up fragments that must be placed in the correct order to solve a problem. Dr Barbara Ericson and Xinying Hou first tested Parson's Problems created from the most common student solution and found that students with lower computer science self-efficacy achieved significantly higher practice performance and in-practice problem-solving efficiency than those without Parson's Problems as scaffolding. They are now testing using generative AI to create a personalised Parson's Problem from incorrect code, as well as different levels of explanation.
Dr Barbara Ericson is a Professor in the School of Information at the University of Michigan. She creates and studies free ebooks with innovative types of practice problems. She is a Distinguished Member of the ACM and won the 2022 SIGCSE Award for Outstanding Contributions to Computer Science Education.
Xinying Hou is currently a third-year PhD student in Information at the University of Michigan, working with Professor Barbara Ericson. She develops innovative learning techniques to support programming learning and applies mixed methods to evaluate their effectiveness. Currently, she is investigating the use of generative AI to provide engaging, comprehensive, personalized, and high-quality scaffolding for novice programmers as they write code.