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Priti Oli; Rabin Banjade; Jeevan Chapagain; Vasile Rus – Grantee Submission, 2024
Assessing students' answers and in particular natural language answers is a crucial challenge in the field of education. Advances in transformer-based models such as Large Language Models (LLMs), have led to significant progress in various natural language tasks. Nevertheless, amidst the growing trend of evaluating LLMs across diverse tasks,…
Descriptors: Student Evaluation, Computer Assisted Testing, Artificial Intelligence, Comprehension
Regan Mozer; Luke Miratrix – Grantee Submission, 2024
For randomized trials that use text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by trained human raters. This process, the current standard, is both time-consuming and limiting: even the largest human coding efforts are typically constrained to…
Descriptors: Artificial Intelligence, Coding, Efficiency, Statistical Inference
Anna Cecilia McWhirter; Katherine A. Hails; David S. DeGarmo; Laura Lee McIntyre; S. Andrew Garbacz; Elizabeth A. Stormshak – Grantee Submission, 2024
Reliable and valid assessment of parenting and child behaviors is critical for clinicians and researchers alike, and observational measures of parenting behaviors are often considered the gold standard for assessing parenting and parent-child interaction quality. The current study sought to evaluate the reliability and validity of the Coder…
Descriptors: Questionnaires, Test Reliability, Test Validity, Kindergarten

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
Kim Luttgen; Kevin Huang; Eunice Chow; Shuangting Yang; Linlin Li – Grantee Submission, 2024
Rural students often face challenges in receiving high-quality education in science, technology, engineering, and math (STEM). Yet without meaningful STEM educational opportunities, rural students might not develop the knowledge and skills needed to compete in a technology-driven workforce. The Learning by Making program (LbyM), an innovative…
Descriptors: Rural Schools, STEM Education, Educational Innovation, Achievement Gap