Publication Date
In 2025 | 0 |
Since 2024 | 3 |
Since 2021 (last 5 years) | 4 |
Since 2016 (last 10 years) | 4 |
Since 2006 (last 20 years) | 4 |
Descriptor
Source
Grantee Submission | 4 |
Author
Jeevan Chapagain | 2 |
Priti Oli | 2 |
Rabin Banjade | 2 |
Vasile Rus | 2 |
Arun-Balajiee… | 1 |
Cai, Zhiqiang | 1 |
Eagan, Brendan | 1 |
Luke Miratrix | 1 |
Mohammad Hassany | 1 |
Regan Mozer | 1 |
Shaffer, David Williamson | 1 |
More ▼ |
Publication Type
Reports - Research | 4 |
Speeches/Meeting Papers | 3 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
Flesch Reading Ease Formula | 1 |
What Works Clearinghouse Rating

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
Cai, Zhiqiang; Siebert-Evenstone, Amanda; Eagan, Brendan; Shaffer, David Williamson – Grantee Submission, 2021
When text datasets are very large, manually coding line by line becomes impractical. As a result, researchers sometimes try to use machine learning algorithms to automatically code text data. One of the most popular algorithms is topic modeling. For a given text dataset, a topic model provides probability distributions of words for a set of…
Descriptors: Coding, Artificial Intelligence, Models, Probability

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