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Arun-Balajiee Lekshmi-Narayanan; Priti Oli; Jeevan Chapagain; Mohammad Hassany; Rabin Banjade; Vasile Rus – Grantee Submission, 2024
Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide…
Descriptors: Coding, Computer Science Education, Computational Linguistics, Artificial Intelligence
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Kole A. Norberg; Husni Almoubayyed; Logan De Ley; April Murphy; Kyle Weldon; Steve Ritter – Grantee Submission, 2024
Large language models (LLMs) offer an opportunity to make large-scale changes to educational content that would otherwise be too costly to implement. The work here highlights how LLMs (in particular GPT-4) can be prompted to revise educational math content ready for large scale deployment in real-world learning environments. We tested the ability…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Educational Change
Neuman, Susan B.; Wong, Kevin M.; Kaefer, Tanya – Grantee Submission, 2017
The purpose of this study was to investigate the influence of digital and non-digital storybooks on low-income preschoolers' oral language comprehension. Employing a within-subject design on 38 four-year-olds from a Head Start program, we compared the effect of medium on preschoolers' target words and comprehension of stories. Four digital…
Descriptors: Oral Language, Story Reading, Low Income Groups, Disadvantaged Youth