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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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Guido Lang; Tamilla Triantoro; Jason H. Sharp – Journal of Information Systems Education, 2024
This study explores the potential of large language models (LLMs), specifically GPT-4 and Gemini, in generating teaching cases for information systems courses. A unique prompt for writing three different types of teaching cases such as a descriptive case, a normative case, and a project-based case on the same IS topic (i.e., the introduction of…
Descriptors: Computational Linguistics, Computer Software, Artificial Intelligence, Readability Formulas
UK Department for Education, 2024
This report sets out the findings of the technical development work completed as part of the Use Cases for Generative AI in Education project, commissioned by the Department for Education (DfE) in September 2023. It has been published alongside the User Research Report, which sets out the findings from the ongoing user engagement activity…
Descriptors: Artificial Intelligence, Technology Uses in Education, Computer Software, Computational Linguistics
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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
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Chou, Shih Yung; Luo, Jiaxi; Ramser, Charles – Journal of International Education in Business, 2021
Purpose: The purpose of this study is to examine student sentiments regarding high-quality vs low-quality teaching. Design/methodology/approach: This study uses a text mining technique to identify the positive and negative patterns of student sentiments from student evaluations of teaching (SET) provided on Ratemyprofessors.com. After identifying…
Descriptors: Educational Quality, Student Attitudes, Positive Attitudes, Negative Attitudes
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Taylor, Z. W. – Journal of Student Affairs Research and Practice, 2019
Research suggests that L2 students (English-language learners) do not access postsecondary education as the same level as L1 (native English-speaking) peers. Using a computational linguistics approach, this study analyzes financial aid application instructions from 341 randomly selected four-year, Title IV-participating U.S. institutions. Results…
Descriptors: Student Financial Aid, Second Language Learning, Achievement Gap, Academic Achievement
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Goh, Tiong-Thye; Sun, Hui; Yang, Bing – Computer Assisted Language Learning, 2020
This study investigates the extent to which microfeatures -- such as basic text features, readability, cohesion, and lexical diversity based on specific word lists -- affect Chinese EFL writing quality. Data analysis was conducted using natural language processing, correlation analysis and stepwise multiple regression analysis on a corpus of 268…
Descriptors: Essays, Writing Tests, English (Second Language), Second Language Learning