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Priti Oli; Rabin Banjade; Jeevan Chapagain; Vasile Rus – Grantee Submission, 2023
This paper systematically explores how Large Language Models (LLMs) generate explanations of code examples of the type used in intro-to-programming courses. As we show, the nature of code explanations generated by LLMs varies considerably based on the wording of the prompt, the target code examples being explained, the programming language, the…
Descriptors: Computational Linguistics, Programming, Computer Science Education, Programming Languages
Mingyu Feng; Neil Heffernan; Kelly Collins; Cristina Heffernan; Robert F. Murphy – Grantee Submission, 2023
Math performance continues to be an important focus for improvement. The most recent National Report Card in the U.S. suggested student math scores declined in the past two years possibly due to COVID-19 pandemic and related school closures. We report on the implementation of a math homework program that leverages AI-based one-to-one technology,…
Descriptors: Homework, Artificial Intelligence, Computer Assisted Instruction, Feedback (Response)
Michelle P. Banawan; Jinnie Shin; Tracy Arner; Renu Balyan; Walter L. Leite; Danielle S. McNamara – Grantee Submission, 2023
Academic discourse communities and learning circles are characterized by collaboration, sharing commonalities in terms of social interactions and language. The discourse of these communities is composed of jargon, common terminologies, and similarities in how they construe and communicate meaning. This study examines the extent to which discourse…
Descriptors: Algebra, Discourse Analysis, Semantics, Syntax