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Found 5 papers, 4 papers with code
Improving Socratic Question Generation using Data Augmentation and Preference Optimization
The Socratic method is a way of guiding students toward solving a problem independently without directly revealing the solution to the problem.
Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education
The goal of our work is to propose a fully automated approach for test case generation that can accurately measure student knowledge, which is important for two reasons.
Multi-Intent Detection in User Provided Annotations for Programming by Examples Systems
This can lead to multiple intents or ambiguity in the input and output samples.
Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank
Reading comprehension is a crucial skill in many aspects of education, including language learning, cognitive development, and fostering early literacy skills in children.
A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing
In this paper, we take a preliminary step towards solving the problem of causal discovery in knowledge tracing, i. e., finding the underlying causal relationship among different skills from real-world student response data.
